Datasets:

ai-conferences
/

MICCAI2022

paper_id string	title string	authors list	miccai_url string	pdf_url string	doi string	sharedit_url string	supplementary_url string	topics list	code_urls list	dataset_urls list	pages string	bibtex large_string	abstract large_string	arxiv_id string	arxiv_id_source string
Paper1347	3D CVT-GAN: A 3D Convolutional Vision Transformer-GAN for PET Reconstruction	[ "Pinxian Zeng", "Luping Zhou", "Chen Zu", "Xinyi Zeng", "Zhengyang Jiao", "Xi Wu", "Jiliu Zhou", "Dinggang Shen", "Yan Wang" ]	https://conferences.miccai.org/2022/papers/001-Paper1347.html	null	10.1007/978-3-031-16446-0_49	https://rdcu.be/cVRTI	null	[ "Image Reconstruction", "Modalities - PET/SPECT", "Organ - Brain" ]	[ "https://github.com/Aru321/CVTGAN" ]	[]	null	null	To obtain high-quality positron emission tomography (PET) scans while reducing potential radiation hazards brought to patients, various generative adversarial network (GAN)-based methods have been developed to reconstruct high-quality standard-dose PET (SPET) images from low-dose PET (LPET) images. However, due to the ...	null	null
Paper1233	3D Global Fourier Network for Alzheimer’s Disease Diagnosis using Structural MRI	[ "Shengjie Zhang", "Xiang Chen", "Bohan Ren", "Haibo Yang", "Ziqi Yu", "Xiao-Yong Zhang", "Yuan Zhou" ]	https://conferences.miccai.org/2022/papers/002-Paper1233.html	null	10.1007/978-3-031-16431-6_4	https://rdcu.be/cVD4L	null	[ "Computer Aided Diagnosis", "Modalities - MRI", "Outcome/disease prediction" ]	[ "https://github.com/qbmizsj/GFNet" ]	[ "https://adni.loni.usc.edu" ]	null	null	Deep learning models, such as convolutional neural networks and self-attention mechanisms, have been shown to be effective in computer-aided diagnosis (CAD) of Alzheimer’s disease (AD) using structural magnetic resonance imaging (sMRI). Most of them use spatial convolutional filters to learn local information from the ...	null	null
Paper1512	4D-OR: Semantic Scene Graphs for OR Domain Modeling	[ "Ege Özsoy", "Evin Pınar Örnek", "Ulrich Eck", "Tobias Czempiel", "Federico Tombari", "Nassir Navab" ]	https://conferences.miccai.org/2022/papers/003-Paper1512.html	null	10.1007/978-3-031-16449-1_45	https://rdcu.be/cVRXk	null	[ "Surgical Scene Understanding", "Surgical Skill and Work Flow Analysis" ]	[ "https://github.com/egeozsoy/4D-OR" ]	[ "https://github.com/egeozsoy/4D-OR" ]	null	null	Surgical procedures are conducted in highly complex operating rooms (OR), comprising different actors, devices, and interactions. To date, only medically trained human experts are capable of understanding all the links and interactions in such a demanding environment. This paper aims to bring the community one step clo...	2203.11937	title_snapshot
Paper2655	A Comprehensive Study of Modern Architectures and Regularization Approaches on CheXpert5000	[ "Sontje Ihler", "Felix Kuhnke", "Svenja Spindeldreier" ]	https://conferences.miccai.org/2022/papers/004-Paper2655.html	null	10.1007/978-3-031-16431-6_62	https://rdcu.be/cVD7i	null	[ "Computer Aided Diagnosis", "Machine Learning - Data efficient Learning", "Machine Learning - Transfer learning", "Modalities - other", "Organ - Lung" ]	[ "https://gitlab.uni-hannover.de/sontje.ihler/chexpert5000" ]	[ "https://stanfordmlgroup.github.io/competitions/chexpert/" ]	null	null	Computer aided diagnosis (CAD) has gained an increased amount of attention in the general research community over the last years as an example of a typical limited data application - with experiments on labeled 100k-200k datasets. Although these datasets are still small compared to natural image datasets like ImageNet1...	2302.06684	title_snapshot
Paper1525	A Deep-Discrete Learning Framework for Spherical Surface Registration	[ "Mohamed A. Suliman", "Logan Z. J. Williams", "Abdulah Fawaz", "Emma C. Robinson" ]	https://conferences.miccai.org/2022/papers/005-Paper1525.html	null	10.1007/978-3-031-16446-0_12	https://rdcu.be/cVRSS	null	[ "Image Registration", "Machine Learning - Semi-/Weakly-/Un-/Self-supervised representation learning" ]	[ "https://github.com/mohamedasuliman/DDR/" ]	[ "https://db.humanconnectome.org/" ]	null	null	Cortical surface registration is a fundamental tool for neuroimaging analysis that has been shown to improve the alignment of functional regions relative to volumetric approaches. Classically, image registration is performed by optimizing a complex objective similarity function, leading to long run times. This contribu...	2203.12999	title_snapshot
Paper1155	A Geometry-Constrainted Deformable Attention Network for Aortic Segmentation	[ "Weiyuan Lin", "Hui Liu", "Lin Gu", "Zhifan Gao" ]	https://conferences.miccai.org/2022/papers/006-Paper1155.html	null	10.1007/978-3-031-16443-9_28	https://rdcu.be/cVRyG	null	[ "Image Segmentation", "Modalities - CT", "Organ - Vessel" ]	[]	[]	null	null	Morphological segmentation of the aorta is significant for aortic diagnosis, intervention, and prognosis. However, it is difficult for existing methods to achieve the continuity of spatial information and the integrity of morphological extraction, due to the gradually variable and irregular geometry of the aorta in the...	null	null
Paper1247	A Hybrid Propagation Network for Interactive Volumetric Image Segmentation	[ "Luyue Shi", "Xuanye Zhang", "Yunbi Liu", "Xiaoguang Han" ]	https://conferences.miccai.org/2022/papers/007-Paper1247.html	null	10.1007/978-3-031-16440-8_64	https://rdcu.be/cVRwR	null	[ "Image Segmentation", "Modalities - CT" ]	[ "https://github.com/luyueshi/Hybrid-Propagation" ]	[ "http://medicaldecathlon.com/", "https://kits19.grand-challenge.org/" ]	null	null	Interactive segmentation is of great importance in clinical practice for correcting and refining the automated segmentation by involving additional user hints, e.g., scribbles and clicks. Currently, interactive segmentation methods for 2D medical images are well studied, while seldom works are conducted on 3D medical v...	null	null
Paper0063	A Learnable Variational Model for Joint Multimodal MRI Reconstruction and Synthesis	[ "Wanyu Bian", "Qingchao Zhang", "Xiaojing Ye", "Yunmei Chen" ]	https://conferences.miccai.org/2022/papers/008-Paper0063.html	null	10.1007/978-3-031-16446-0_34	https://rdcu.be/cVRTt	null	[ "Image Reconstruction", "Machine Learning - Other", "Organ - Brain" ]	[]	[ "https://www.med.upenn.edu/sbia/brats2018/data.html" ]	null	null	Generating multi-contrasts/modal MRI of the same anatomy enriches diagnostic information but is limited in practice due to excessive data acquisition time. In this paper, we propose a novel deep-learning model for joint reconstruction and synthesis of multi-modal MRI using incomplete k-space data of several source moda...	2204.03804	title_snapshot
Paper1374	A Medical Semantic-Assisted Transformer for Radiographic Report Generation	[ "Zhanyu Wang", "Mingkang Tang", "Lei Wang", "Xiu Li", "Luping Zhou" ]	https://conferences.miccai.org/2022/papers/009-Paper1374.html	null	10.1007/978-3-031-16437-8_63	https://rdcu.be/cVRuO	null	[ "Computer Aided Diagnosis", "Modalities - Text (clinical/radiology reports)" ]	[ "https://github.com/zwan0839/MSAT" ]	[ "https://drive.google.com/file/d/1DS6NYirOXQf8qYieSVMvqNwuOlgAbM_E/view" ]	null	null	Automated radiographic report generation is a challenging cross-domain task that aims to automatically generate accurate and semantic-coherence reports to describe medical images. Despite the recent progress in this field, there are still many challenges at least in the following aspects. First, radiographic images are...	2208.10358	title_snapshot
Paper1785	A Multi-task Network with Weight Decay Skip Connection Training for Anomaly Detection in Retinal Fundus Images	[ "Wentian Zhang", "Xu Sun", "Yuexiang Li", "Haozhe Liu", "Nanjun He", "Feng Liu", "Yefeng Zheng" ]	https://conferences.miccai.org/2022/papers/010-Paper1785.html	null	10.1007/978-3-031-16434-7_63	https://rdcu.be/cVRsv	null	[ "Machine Learning - Semi-/Weakly-/Un-/Self-supervised representation learning", "Image Reconstruction", "Organ - Eye", "Outcome/disease prediction" ]	[ "https://github.com/WentianZhang-ML/WDMT-Net" ]	[]	null	null	By introducing the skip connection to bridge the semantic gap between encoder and decoder, U-shape architecture has been proven to be effective for recovering fine-grained details in dense prediction tasks. However, such a mechanism cannot be directly applied to reconstruction-based anomaly detection, since the skip co...	null	null
Paper1016	A New Dataset and A Baseline Model for Breast Lesion Detection in Ultrasound Videos	[ "Zhi Lin", "Junhao Lin", "Lei Zhu", "Huazhu Fu", "Jing Qin", "Liansheng Wang" ]	https://conferences.miccai.org/2022/papers/011-Paper1016.html	null	10.1007/978-3-031-16437-8_59	https://rdcu.be/cVRuK	null	[ "Modalities - Video", "Machine Learning - Other", "Modalities - Ultrasound", "Organ - Breast" ]	[ "https://github.com/jhl-Det/CVA-Net" ]	[ "https://pan.baidu.com/s/1yYME7-DvvIEZzCb72NXaJA?pwd=jnie" ]	null	null	Breast lesion detection in ultrasound is critical for breast cancer diagnosis. Existing methods mainly rely on individual 2D ultrasound images or combine unlabeled video and labeled 2D images to train models for breast lesion detection. In this paper, we first collect and annotate an ultrasound video dataset (188 video...	2207.00141	title_snapshot
Paper0202	A Novel Deep Learning System for Breast Lesion Risk Stratification in Ultrasound Images	[ "Ting Liu", "Xing An", "Yanbo Liu", "Yuxi Liu", "Bin Lin", "Runzhou Jiang", "Wenlong Xu", "Longfei Cong", "Lei Zhu" ]	https://conferences.miccai.org/2022/papers/012-Paper0202.html	null	10.1007/978-3-031-16437-8_45	https://rdcu.be/cVRuw	null	[ "Computer Aided Diagnosis", "Modalities - Ultrasound", "Organ - Breast", "Organ - Tumor" ]	[]	[]	null	null	This paper presents a novel deep learning system to classify breast lesions in ultrasound images into benign and malignant and into Breast Imaging Reporting and Data System (BI-RADS) six categories simultaneously. A multitask soft label generating architecture is proposed to improve the classification performance, in w...	null	null
Paper0975	A Novel Fusion Network for Morphological Analysis of Common Iliac Artery	[ "Meng Song", "Shi-Qi Liu", "Xiao-Liang Xie", "Xiao-Hu Zhou", "Zeng-Guang Hou", "Yan-Jie Zhou", "Xi-Yao Ma" ]	https://conferences.miccai.org/2022/papers/013-Paper0975.html	null	10.1007/978-3-031-16449-1_6	https://rdcu.be/cVRUM	null	[ "Image Segmentation", "Computer Aided Diagnosis", "Image Analysis in Robot-Assisted Surgery", "Modalities - other", "Organ - Vessel" ]	[ "https://github.com/SongCASIA/FTU_Net" ]	[]	null	null	In endovascular interventional therapy, automatic common iliac artery morphological analysis can help physicians plan surgical procedures and assist in the selection of appropriate stents to improve surgical safety. However, different people have distinct blood vessel shapes, and many patients have severe malformations...	null	null
Paper1405	A Novel Knowledge Keeper Network for 7T-Free But 7T-Guided Brain Tissue Segmentation	[ "Jieun Lee", "Kwanseok Oh", "Dinggang Shen", "Heung-Il Suk" ]	https://conferences.miccai.org/2022/papers/014-Paper1405.html	null	10.1007/978-3-031-16443-9_32	https://rdcu.be/cVRyM	null	[ "Machine Learning - Transfer learning", "Image Segmentation", "Modalities - MRI", "Organ - Brain" ]	[ "https://github.com/2jieun2/knowledge_keeper" ]	[ "https://www.nitrc.org/projects/ibsr" ]	null	null	An increase in signal-to-noise ratio (SNR) and susceptibility-induced contrast at higher field strengths, e.g., 7T, is crucial for medical image analysis by providing better insights for the pathophysiology, diagnosis, and treatment of several disease entities. However, it is difficult to obtain 7T images in real clini...	null	null
Paper1881	A Penalty Approach for Normalizing Feature Distributions to Build Confounder-Free Models	[ "Anthony Vento", "Qingyu Zhao", "Robert Paul", "Kilian M. Pohl", "Ehsan Adeli" ]	https://conferences.miccai.org/2022/papers/015-Paper1881.html	null	10.1007/978-3-031-16437-8_37	https://rdcu.be/cVRtn	null	[ "Outcome/disease prediction", "Computer Aided Diagnosis", "Modalities - MRI", "Organ - Brain" ]	[ "https://github.com/vento99" ]	[ "https://github.com/mlu355/MetadataNorm/blob/main/synthetic_dataset.py", "https://adni.loni.usc.edu/" ]	null	null	Translating machine learning algorithms into clinical applications requires addressing challenges related to interpretability, such as accounting for the effect of confounding variables (or metadata). Confounding variables affect the relationship between input training data and target outputs. When we train a model on ...	2207.04607	title_snapshot
Paper2636	A Projection-Based K-space Transformer Network for Undersampled Radial MRI Reconstruction with Limited Training Subjects	[ "Chang Gao", "Shu-Fu Shih", "J. Paul Finn", "Xiaodong Zhong" ]	https://conferences.miccai.org/2022/papers/016-Paper2636.html	null	10.1007/978-3-031-16446-0_69	https://rdcu.be/cVRUd	null	[ "Image Reconstruction", "Machine Learning - Data efficient Learning", "Machine Learning - Model Generalizability", "Modalities - MRI", "Organ - Abdomen" ]	[]	[]	null	null	The recent development of deep learning combined with compressed sensing enables fast reconstruction of undersampled MR images and has achieved state-of-the-art performance for Cartesian k-space trajectories. However, non-Cartesian trajectories such as the radial trajectory need to be transformed onto a Cartesian grid ...	2206.07219	title_snapshot
Paper0545	A Robust Volumetric Transformer for Accurate 3D Tumor Segmentation	[ "Himashi Peiris", "Munawar Hayat", "Zhaolin Chen", "Gary Egan", "Mehrtash Harandi" ]	https://conferences.miccai.org/2022/papers/017-Paper0545.html	null	10.1007/978-3-031-16443-9_16	https://rdcu.be/cVRyu	null	[ "Image Segmentation", "Modalities - MRI", "Organ - Brain", "Organ - Tumor" ]	[ "https://github.com/himashi92/VT-UNet" ]	[ "http://medicaldecathlon.com/" ]	null	null	We propose a Transformer architecture for volumetric segmentation, a challenging task that requires keeping a complex balance in encoding local and global spatial cues, and preserving information along all axes of the volume. Encoder of the proposed design benefits from self-attention mechanism to simultaneously encode...	2111.13300	title_snapshot
Paper0114	A Self-Guided Framework for Radiology Report Generation	[ "Jun Li", "Shibo Li", "Ying Hu", "Huiren Tao" ]	https://conferences.miccai.org/2022/papers/018-Paper0114.html	null	10.1007/978-3-031-16452-1_56	https://rdcu.be/cVVqc	null	[ "Modalities - Text (clinical/radiology reports)", "Computer Aided Diagnosis", "Organ - Lung" ]	[ "https://github.com/LijunRio/A-Self-Guided-Framework" ]	[ "https://openi.nlm.nih.gov/" ]	null	null	Automatic radiology report generation is essential to computer-aided diagnosis. Through the success of image captioning, medical report generation has been achievable. However, the lack of annotated disease labels is still the bottleneck of this area. In addition, the image-text data bias problem and complex sentences ...	2206.09378	title_snapshot
Paper0199	A Sense of Direction in Biomedical Neural Networks	[ "Zewen Liu", "Timothy F. Cootes" ]	https://conferences.miccai.org/2022/papers/019-Paper0199.html	null	10.1007/978-3-031-16443-9_8	https://rdcu.be/cVRye	null	[ "Image Segmentation", "Machine Learning - Interpretability / Explainability", "Organ - Eye", "Organ - Vessel" ]	[ "https://github.com/Zewen-Liu/MASC-Unit" ]	[ "https://blogs.kingston.ac.uk/retinal/chasedb1/", "https://drive.grand-challenge.org/", "https://monuseg.grand-challenge.org/Data/" ]	null	null	We describe an approach to making a model be aware of not only intensity but also properties such as direction and scale during forward propagation. Such properties are important in when analysing images containing curvilinear structures such as vessels or fibres. We propose the General Multi-Angle Scale Convolution (G...	null	null
Paper0395	A Spatiotemporal Model for Precise and Efficient Fully-automatic 3D Motion Correction in OCT	[ "Stefan Ploner", "Siyu Chen", "Jungeun Won", "Lennart Husvogt", "Katharina Breininger", "Julia Schottenhamml", "James Fujimoto", "Andreas Maier" ]	https://conferences.miccai.org/2022/papers/020-Paper0395.html	null	10.1007/978-3-031-16434-7_50	https://rdcu.be/cVRsi	null	[ "Image Registration", "Modalities - Biophotonics", "Organ - Eye" ]	[]	[]	null	null	Optical coherence tomography (OCT) is a micrometer-scale, volumetric imaging modality that has become a clinical standard in ophthalmology. OCT instruments image by raster-scanning a focused light spot across the retina, acquiring sequential cross-sectional images to generate volumetric data. Patient eye motion during ...	2209.07232	title_snapshot
Paper0893	A Transformer-Based Iterative Reconstruction Model for Sparse-View CT Reconstruction	[ "Wenjun Xia", "Ziyuan Yang", "Qizheng Zhou", "Zexin Lu", "Zhongxian Wang", "Yi Zhang" ]	https://conferences.miccai.org/2022/papers/021-Paper0893.html	null	10.1007/978-3-031-16446-0_75	https://rdcu.be/cVRUj	null	[ "Image Reconstruction", "Modalities - CT" ]	[ "https://github.com/Deep-Imaging-Group/RegFormer" ]	[]	null	null	Sparse-view computed tomography (CT) is one of the primary means to reduce the radiation risk. But the reconstruction of sparse-view CT will be contaminated by severe artifacts. By carefully designing the regularization terms, the iterative reconstruction (IR) algorithm can achieve promising results. With the introduct...	null	null
Paper0036	AANet: Artery-Aware Network for Pulmonary Embolism Detection in CTPA Images	[ "Jia Guo", "Xinglong Liu", "Yinan Chen", "Shaoting Zhang", "Guangyu Tao", "Hong Yu", "Huiyuan Zhu", "Wenhui Lei", "Huiqi Li", "Na Wang" ]	https://conferences.miccai.org/2022/papers/022-Paper0036.html	null	10.1007/978-3-031-16431-6_45	https://rdcu.be/cVD61	null	[ "Computer Aided Diagnosis", "Image Segmentation", "Machine Learning - Data efficient Learning", "Modalities - CT", "Organ - Lung" ]	[ "https://github.com/guojiajeremy/AANet" ]	[ "https://ieee-dataport.org/open-access/cad-pe", "https://www.kaggle.com/datasets/andrewmvd/pulmonary-embolism-in-ct-images" ]	null	null	Pulmonary embolism (PE) is life-threatening and computed tomography pulmonary angiography (CTPA) is the best diagnostic techniques in clinics. However, PEs usually appear as dark spots among the bright regions of blood arteries in CTPA images, which can be very similar with veins that are less bright and soft tissues. ...	null	null
Paper2394	Accelerated pseudo 3D dynamic speech MR imaging at 3T using unsupervised deep variational manifold learning	[ "Rushdi Zahid Rusho", "Qing Zou", "Wahidul Alam", "Subin Erattakulangara", "Mathews Jacob", "Sajan Goud Lingala" ]	https://conferences.miccai.org/2022/papers/023-Paper2394.html	null	10.1007/978-3-031-16446-0_66	https://rdcu.be/cVRUa	null	[ "Image Reconstruction", "Machine Learning - Semi-/Weakly-/Un-/Self-supervised representation learning", "Modalities - MRI", "Organ - Other", "Visualisation in Biomedical Imaging" ]	[ "https://github.com/rushdi-rusho/varMRI" ]	[ "https://github.com/rushdi-rusho/varMRI/tree/main/SpeechDatasets" ]	null	null	Magnetic resonance imaging (MRI) of vocal tract shaping and surrounding articulators during speaking is a powerful tool in several application areas such as understanding language disorder, informing treatment plans in oro-pharyngeal cancers. However, this is a challenging task due to fundamental tradeoffs between spat...	null	null
Paper2344	Accurate and Explainable Image-based Prediction Using a Lightweight Generative Model	[ "Chiara Mauri", "Stefano Cerri", "Oula Puonti", "Mark Mühlau", "Koen Van Leemput" ]	https://conferences.miccai.org/2022/papers/024-Paper2344.html	null	10.1007/978-3-031-16452-1_43	https://rdcu.be/cVVpY	null	[ "Outcome/disease prediction", "Machine Learning - Data efficient Learning", "Machine Learning - Interpretability / Explainability", "Modalities - MRI", "Organ - Brain" ]	[]	[ "https://www.ukbiobank.ac.uk" ]	null	null	Recent years have seen a growing interest in methods for predicting a variable of interest, such as a subject’s age, from individual brain scans. Although the field has focused strongly on nonlinear discriminative methods using deep learning, here we explore whether linear generative techniques can be used as practical...	null	null
Paper0249	Accurate and Robust Lesion RECIST Diameter Prediction and Segmentation with Transformers	[ "Youbao Tang", "Ning Zhang", "Yirui Wang", "Shenghua He", "Mei Han", "Jing Xiao", "Ruei-Sung Lin" ]	https://conferences.miccai.org/2022/papers/025-Paper0249.html	null	10.1007/978-3-031-16440-8_51	https://rdcu.be/cVRwC	null	[ "Image Segmentation", "Computer Aided Diagnosis", "Machine Learning - Semi-/Weakly-/Un-/Self-supervised representation learning", "Modalities - CT" ]	[]	[ "https://nihcc.app.box.com/v/DeepLesion", "https://github.com/JimmyCai91/DLT" ]	null	null	Automatically measuring lesion/tumor size with RECIST (Response Evaluation Criteria In Solid Tumors) diameters and segmentation is important for computer-aided diagnosis. Although it has been studied in recent years, there is still space to improve its accuracy and robustness, such as (1) enhancing features by incorpor...	2208.13113	title_snapshot
Paper1270	Accurate Corresponding Fiber Tract Segmentation via FiberGeoMap Learner	[ "Zhenwei Wang", "Yifan Lv", "Mengshen He", "Enjie Ge", "Ning Qiang", "Bao Ge" ]	https://conferences.miccai.org/2022/papers/026-Paper1270.html	null	10.1007/978-3-031-16431-6_14	https://rdcu.be/cVD4V	null	[ "Organ - Brain", "Image Segmentation", "Modalities - MRI" ]	[ "https://github.com/Garand0o0/FiberTractSegmentation" ]	[]	null	null	Fiber tract segmentation is a prerequisite for the tract-based statistical analysis and plays a crucial role in understanding brain structure and function. The previous researches mainly consist of two steps: defining and computing the similarity features of fibers, and then adopting machine learning algorithm for clus...	null	null
Paper0159	ACT: Semi-supervised Domain-adaptive Medical Image Segmentation with Asymmetric Co-Training	[ "Xiaofeng Liu", "Fangxu Xing", "Nadya Shusharina", "Ruth Lim", "C.-C. Jay Kuo", "Georges El Fakhri", "Jonghye Woo" ]	https://conferences.miccai.org/2022/papers/027-Paper0159.html	null	10.1007/978-3-031-16443-9_7	https://rdcu.be/cVRyd	null	[ "Image Segmentation", "Machine Learning - Semi-/Weakly-/Un-/Self-supervised representation learning", "Machine Learning - Transfer learning", "Modalities - MRI", "Organ - Brain" ]	[]	[]	null	null	Unsupervised domain adaptation (UDA) has been vastly explored to address domain shifts between source and target domains, by applying a well-performed model in an unlabeled target domain via supervision of a labeled source domain. Recent literature, however, has indicated that the performance is still far from satisfac...	2206.02288	title_snapshot
Paper2509	Adaptation of Surgical Activity Recognition Models Across Operating Rooms	[ "Ali Mottaghi", "Aidean Sharghi", "Serena Yeung", "Omid Mohareri" ]	https://conferences.miccai.org/2022/papers/028-Paper2509.html	null	10.1007/978-3-031-16449-1_51	https://rdcu.be/cVRXp	null	[ "Surgical Skill and Work Flow Analysis", "Machine Learning - Model Generalizability", "Machine Learning - Semi-/Weakly-/Un-/Self-supervised representation learning", "Surgical Data Science", "Surgical Scene Understanding" ]	[]	[]	null	null	Automatic surgical activity recognition enables more intelligent surgical devices and a more efficient workflow. Integration of such technology in new operating rooms has the potential to improve care delivery to patients and decrease costs. Recent works have achieved a promising performance on surgical activity recogn...	2207.03083	title_snapshot
Paper2006	Adapting the Mean Teacher for keypoint-based lung registration under geometric domain shifts	[ "Alexander Bigalke", "Lasse Hansen", "Mattias P. Heinrich" ]	https://conferences.miccai.org/2022/papers/029-Paper2006.html	null	10.1007/978-3-031-16446-0_27	https://rdcu.be/cVRS9	null	[ "Image Registration", "Machine Learning - Transfer learning", "Organ - Lung" ]	[ "https://github.com/multimodallearning/registration-da-mean-teacher" ]	[ "https://learn2reg.grand-challenge.org/Learn2Reg2021/", "https://med.emory.edu/departments/radiation-oncology/research-laboratories/deformable-image-registration/downloads-and-reference-data/index.html" ]	null	null	Recent deep learning-based methods for medical image registration achieve results that are competitive with conventional optimization algorithms at reduced run times. However, deep neural networks generally require plenty of labeled training data and are vulnerable to domain shifts between training and test data. While...	2207.00371	title_snapshot
Paper0272	Adaptive 3D Localization of 2D Freehand Ultrasound Brain Images	[ "Pak-Hei Yeung", "Moska Aliasi", "Monique Haak", "the INTERGROWTH-21st Consortium", "Weidi Xie", "Ana I. L. Namburete" ]	https://conferences.miccai.org/2022/papers/030-Paper0272.html	null	10.1007/978-3-031-16440-8_20	https://rdcu.be/cVRvL	null	[ "Modalities - Ultrasound", "Computer Aided Diagnosis", "Machine Learning - Transfer learning", "Organ - Brain" ]	[ "https://github.com/pakheiyeung/AdLocUI" ]	[]	null	null	Two-dimensional (2D) freehand ultrasound is the mainstay in prenatal care and fetal growth monitoring. The task of matching corresponding cross-sectional planes in the 3D anatomy for a given 2D ultrasound brain scan is essential in freehand scanning, but challenging. We propose AdLocUI, a framework that Adaptively Loca...	2209.05477	title_snapshot
Paper2848	AdaTriplet: Adaptive Gradient Triplet Loss with Automatic Margin Learning for Forensic Medical Image Matching	[ "Khanh Nguyen", "Huy Hoang Nguyen", "Aleksei Tiulpin" ]	https://conferences.miccai.org/2022/papers/031-Paper2848.html	null	10.1007/978-3-031-16452-1_69	https://rdcu.be/cVVqq	null	[ "Machine Learning - Other", "Machine Learning - Semi-/Weakly-/Un-/Self-supervised representation learning", "Modalities - other", "Organ - Breast", "Organ - Lung", "Organ - Musculoskeleton" ]	[ "https://github.com/Oulu-IMEDS/AdaTriplet" ]	[ "https://nda.nih.gov/oai/", "https://nihcc.app.box.com/v/ChestXray-NIHCC" ]	null	null	This paper tackles the challenge of forensic medical image matching (FMIM) using deep neural networks (DNNs). FMIM is a particular case of content-based image retrieval (CBIR). The main challenge in FMIM compared to the general case of CBIR, is that the subject to whom a query image belongs may be affected by aging and...	2205.02849	title_snapshot
Paper1371	Addressing Class Imbalance in Semi-supervised Image Segmentation: A Study on Cardiac MRI	[ "Hritam Basak", "Sagnik Ghosal", "Ram Sarkar" ]	https://conferences.miccai.org/2022/papers/032-Paper1371.html	null	10.1007/978-3-031-16452-1_22	https://rdcu.be/cVRY4	null	[ "Image Segmentation", "Machine Learning - Semi-/Weakly-/Un-/Self-supervised representation learning" ]	[]	[ "https://www.creatis.insa-lyon.fr/Challenge/acdc/databases.html", "http://www.sdspeople.fudan.edu.cn/zhuangxiahai/0/mmwhs/" ]	null	null	Due to the imbalanced and limited data, semi-supervised medical image segmentation methods often fail to produce superior performance for some specific tailed classes. Inadequate training for those particular classes could introduce more noise to the generated pseudo labels, affecting overall learning. To alleviate thi...	2209.00123	title_snapshot
Paper0332	Adversarial Consistency for Single Domain Generalization in Medical Image Segmentation	[ "Yanwu Xu", "Shaoan Xie", "Maxwell Reynolds", "Matthew Ragoza", "Mingming Gong", "Kayhan Batmanghelich" ]	https://conferences.miccai.org/2022/papers/033-Paper0332.html	null	10.1007/978-3-031-16449-1_64	https://rdcu.be/cVRXC	null	[ "Machine Learning - Transfer learning", "Image Segmentation" ]	[]	[]	null	null	An organ segmentation method that can generalize to unseen contrasts and scanner settings can significantly reduce the need for retraining of deep learning models. Domain Generalization (DG) aims to achieve this goal. However, most DG methods for segmentation require training data from multiple domains during training....	2206.13737	title_snapshot
Paper1829	Adversarially Robust Prototypical Few-shot Segmentation with Neural-ODEs	[ "Prashant Pandey", "Aleti Vardhan", "Mustafa Chasmai", "Tanuj Sur", "Brejesh Lall" ]	https://conferences.miccai.org/2022/papers/034-Paper1829.html	null	10.1007/978-3-031-16452-1_8	https://rdcu.be/cVRYL	null	[ "Machine Learning - Data efficient Learning", "Image Segmentation", "Machine Learning - Model Generalizability", "Modalities - CT", "Modalities - MRI", "Organ - Abdomen", "Organ - Lung" ]	[ "https://github.com/prinshul/Prototype_NeuralODE_Adv_Attack" ]	[]	null	null	Few-shot Learning (FSL) methods are being adopted in settings where data is not abundantly available. This is especially seen in medical domains where the annotations are expensive to obtain. Deep Neural Networks have been shown to be vulnerable to adversarial attacks. This is even more severe in the case of FSL due to...	2210.03429	title_snapshot
Paper1248	Agent with Tangent-based Formulation and Anatomical Perception for Standard Plane Localization in 3D Ultrasound	[ "Yuxin Zou", "Haoran Dou", "Yuhao Huang", "Xin Yang", "Jikuan Qian", "Chaojiong Zhen", "Xiaodan Ji", "Nishant Ravikumar", "Guoqiang Chen", "Weijun Huang", "Alejandro F. Frangi", "Dong Ni" ]	https://conferences.miccai.org/2022/papers/035-Paper1248.html	null	10.1007/978-3-031-16440-8_29	https://rdcu.be/cVRvU	null	[ "Machine Learning - Reinforcement learning", "Modalities - Ultrasound", "Organ - Fetal", "Organ - Other" ]	[]	[]	null	null	Standard plane (SP) localization is essential in routine clinical ultrasound (US) diagnosis. Compared to 2D US, 3D US can acquire multiple view planes in one scan and provide complete anatomy with the addition of coronal plane. However, manually navigating SPs in 3D US is laborious and biased due to the orientation var...	2207.00475	title_snapshot
Paper1552	Aggregative Self-Supervised Feature Learning from Limited Medical Images	[ "Jiuwen Zhu", "Yuexiang Li", "Lian Ding", "S. Kevin Zhou" ]	https://conferences.miccai.org/2022/papers/036-Paper1552.html	null	10.1007/978-3-031-16452-1_6	https://rdcu.be/cVRYJ	null	[ "Machine Learning - Semi-/Weakly-/Un-/Self-supervised representation learning" ]	[]	[]	null	null	Limited training data and annotation shortage are the main challenges for the development of automated medical image analysis systems. As a potential solution, self-supervised learning (SSL) causes an increasing attention from the community. The key part in SSL is its proxy task that defines the supervisory signals and...	2012.07477	title_judge
Paper1305	An Accurate Unsupervised Liver Lesion Detection Method Using Pseudo-Lesions	[ "He Li", "Yutaro Iwamoto", "Xianhua Han", "Lanfen Lin", "Hongjie Hu", "Yen-Wei Chen" ]	https://conferences.miccai.org/2022/papers/037-Paper1305.html	null	10.1007/978-3-031-16452-1_21	https://rdcu.be/cVRY3	null	[ "Machine Learning - Semi-/Weakly-/Un-/Self-supervised representation learning", "Image Reconstruction", "Modalities - CT", "Organ - Abdomen", "Organ - Tumor" ]	[]	[]	null	null	Anomaly detection using an unsupervised learning scheme has become a challenging research topic. Unsupervised learning requires only unlabeled normal data for training and can detect anomalies in unseen testing data. In this paper, we propose an unsupervised liver lesion detection framework based on generative adversar...	null	null
Paper1415	An adaptive network with extragradient for diffusion MRI-based microstructure estimation	[ "Tianshu Zheng", "Weihao Zheng", "Yi Sun", "Yi Zhang", "Chuyang Ye", "Dan Wu" ]	https://conferences.miccai.org/2022/papers/038-Paper1415.html	null	10.1007/978-3-031-16431-6_15	https://rdcu.be/cVD4X	null	[ "Modalities - MRI", "Machine Learning - Other", "Organ - Brain" ]	[]	[]	null	null	Diffusion MRI (dMRI) is a powerful tool for probing tissue microstructural properties. However, advanced dMRI models are commonly nonlinear and complex, which requires densely sampled q-space and is prone to estimation errors. This problem can be resolved using deep learning techniques, especially optimization-based ne...	null	null
Paper2756	An Advanced Deep Learning Framework for Video-based Diagnosis of ASD	[ "Miaomiao Cai", "Mingxing Li", "Zhiwei Xiong", "Pengju Zhao", "Enyao Li", "Jiulai Tang" ]	https://conferences.miccai.org/2022/papers/039-Paper2756.html	null	10.1007/978-3-031-16440-8_42	https://rdcu.be/cVRwt	null	[ "Computer Aided Diagnosis", "Modalities - Video", "Outcome/disease prediction" ]	[ "https://github.com/xiaotaiyangcmm/DASD" ]	[ "https://github.com/xiaotaiyangcmm/DASD" ]	null	null	Autism spectrum disorder (ASD) is one of the most common neurodevelopmental disorders, which impairs the communication and interaction ability of patients. Intensive intervention in early ASD can effectively improve symptoms, so the diagnosis of ASD children receives significant attention. However, clinical assessment ...	null	null
Paper0031	An End-to-End Combinatorial Optimization Method for R-band Chromosome Recognition with Grouping Guided Attention	[ "Chao Xia", "Jiyue Wang", "Yulei Qin", "Yun Gu", "Bing Chen", "Jie Yang" ]	https://conferences.miccai.org/2022/papers/040-Paper0031.html	null	10.1007/978-3-031-16440-8_1	https://rdcu.be/cVRvl	null	[ "Modalities - Microscopy", "Computer Aided Diagnosis", "Outcome/disease prediction", "Population Imaging and Imaging Genetics" ]	[ "https://github.com/xiabc612/R-band-chromosome-recognition" ]	[]	null	null	Chromosome recognition is a critical and time-consuming process in karyotyping, especially for R-band chromosomes with poor visualization quality. Existing computer-aided chromosome recognition methods mainly focus on better feature representation of individual chromosomes while neglecting the fact that chromosomes fro...	null	null
Paper0049	An Inclusive Task-Aware Framework for Radiology Report Generation	[ "Lin Wang", "Munan Ning", "Donghuan Lu", "Dong Wei", "Yefeng Zheng", "Jie Chen" ]	https://conferences.miccai.org/2022/papers/041-Paper0049.html	null	10.1007/978-3-031-16452-1_54	https://rdcu.be/cVVqa	null	[ "Modalities - Text (clinical/radiology reports)" ]	[ "https://github.com/Reremee/ITA" ]	[]	null	null	To avoid the tedious and laborious radiology report writing, the automatic generation of radiology reports has drawn great attention recently. Previous studies attempted to directly transfer the image captioning method to radiology report generation given the apparent similarity between these two tasks. Although these ...	null	null
Paper1549	An Optimal Control Problem for Elastic Registration and Force Estimation in Augmented Surgery	[ "Guillaume Mestdagh", "Stéphane Cotin" ]	https://conferences.miccai.org/2022/papers/042-Paper1549.html	null	10.1007/978-3-031-16449-1_8	https://rdcu.be/cVRUO	null	[ "Image Registration", "Image-Guided Interventions", "Interventional Simulation Systems", "Organ - Abdomen" ]	[ "https://github.com/gmestdagh/adjoint-elastic-registration" ]	[]	null	null	The nonrigid alignment between a pre-operative biomechanical model and an intra-operative observation is a critical step to track the motion of a soft organ in augmented surgery. While many elastic registration procedures introduce artificial forces into the direct physical model to drive the registration, we propose i...	2206.10931	title_snapshot
Paper1625	Analyzing and Improving Low Dose CT Denoising Network via HU Level Slicing	[ "Sutanu Bera", "Prabir Kumar Biswas" ]	https://conferences.miccai.org/2022/papers/043-Paper1625.html	null	10.1007/978-3-031-16446-0_56	https://rdcu.be/cVRTZ	null	[ "Image Reconstruction", "Machine Learning - Interpretability / Explainability", "Machine Learning - Model Generalizability", "Modalities - CT" ]	[]	[]	null	null	The deep convolutional neural network has been extensively studied for medical images denoising, specifically for low dose CT(LDCT) denoising. However, most of them disregard that medical images have a large dynamic range. After normalizing the input image, the difference between two nearby HU levels becomes minimal; f...	null	null
Paper1703	Analyzing Brain Structural Connectivity as Continuous Random Functions	[ "William Consagra", "Martin Cole", "Zhengwu Zhang" ]	https://conferences.miccai.org/2022/papers/044-Paper1703.html	null	10.1007/978-3-031-16452-1_27	https://rdcu.be/cVRY9	null	[ "Machine Learning - Data efficient Learning", "Modalities - MRI", "Organ - Brain" ]	[ "https://github.com/sbci-brain/SBCI_Modeling_FPCA" ]	[ "https://db.humanconnectome.org" ]	null	null	This work considers a continuous framework to characterize the population-level variability of structural connectivity. Our framework assumes the observed white matter fiber tract endpoints are driven by a latent random function defined over a product manifold domain. To overcome the computational challenges of analyzi...	2206.11191	title_snapshot
Paper1726	Anatomy-Guided Weakly-Supervised Abnormality Localization in Chest X-rays	[ "Ke Yu", "Shantanu Ghosh", "Zhexiong Liu", "Christopher Deible", "Kayhan Batmanghelich" ]	https://conferences.miccai.org/2022/papers/045-Paper1726.html	null	10.1007/978-3-031-16443-9_63	https://rdcu.be/cVRzh	null	[ "Machine Learning - Semi-/Weakly-/Un-/Self-supervised representation learning", "Computer Aided Diagnosis", "Machine Learning - Data efficient Learning", "Machine Learning - Interpretability / Explainability", "Modalities - Text (clinical/radiology reports)", "Organ - Lung", "Outcome/disease prediction"...	[ "https://github.com/batmanlab/AGXNet" ]	[]	null	null	Creating a large-scale dataset of abnormality annotation on medical images is a labor-intensive and costly task. Leveraging weak supervision from readily available data such as radiology reports can compensate lack of large-scale data for anomaly detection methods. However, most of the current methods only use image-le...	2206.12704	title_snapshot
Paper2430	Anomaly-aware multiple instance learning for rare anemia disorder classification	[ "Salome Kazeminia", "Ario Sadafi", "Asya Makhro", "Anna Bogdanova", "Shadi Albarqouni", "Carsten Marr" ]	https://conferences.miccai.org/2022/papers/046-Paper2430.html	null	10.1007/978-3-031-16452-1_33	https://rdcu.be/cVVpG	null	[ "Machine Learning - Semi-/Weakly-/Un-/Self-supervised representation learning", "Computer Aided Diagnosis", "Machine Learning - Interpretability / Explainability", "Modalities - Microscopy" ]	[ "https://github.com/marrlab/Anomaly-aware-MIL" ]	[]	null	null	Deep learning-based classification of rare anemia disorders is challenged by the lack of training data and instance-level annotations. Multiple Instance Learning (MIL) has shown to be an effective solution, yet it suffers from low accuracy and limited explainability. Although the inclusion of attention mechanisms has a...	2207.01742	title_snapshot
Paper1683	Assessing the Performance of Automated Prediction and Ranking of Patient Age from Chest X-rays Against Clinicians	[ "Matthew MacPherson", "Keerthini Muthuswamy", "Ashik Amlani", "Charles Hutchinson", "Vicky Goh", "Giovanni Montana" ]	https://conferences.miccai.org/2022/papers/047-Paper1683.html	null	10.1007/978-3-031-16449-1_25	https://rdcu.be/cVRU5	null	[ "Machine Learning - Other", "Machine Learning - Interpretability / Explainability", "Machine Learning - Model Generalizability", "Modalities - other", "Organ - Heart", "Organ - Lung", "Visualisation in Biomedical Imaging" ]	[]	[]	null	null	Understanding the internal physiological changes accompanying the aging process is an important aspect of medical image interpretation, with the expected changes acting as a baseline when reporting abnormal findings. Deep learning has recently been demonstrated to allow the accurate estimation of patient age from chest...	2207.01302	title_snapshot
Paper1796	Asymmetry Disentanglement Network for Interpretable Acute Ischemic Stroke Infarct Segmentation in Non-Contrast CT Scans	[ "Haomiao Ni", "Yuan Xue", "Kelvin Wong", "John Volpi", "Stephen T.C. Wong", "James Z. Wang", "Xiaolei Huang" ]	https://conferences.miccai.org/2022/papers/048-Paper1796.html	null	10.1007/978-3-031-16452-1_40	https://rdcu.be/cVVpV	null	[ "Image Segmentation", "Modalities - CT", "Organ - Brain" ]	[ "https://github.com/nihaomiao/MICCAI22_ADN" ]	[]	null	null	Accurate infarct segmentation in non-contrast CT (NCCT) images is a crucial step toward computer-aided acute ischemic stroke (AIS) assessment. In clinical practice, bilateral symmetric comparison of brain hemispheres is usually used to locate pathological abnormalities. Recent research has explored asymmetries to assis...	2206.15445	title_snapshot
Paper2434	Atlas-based Semantic Segmentation of Prostate Zones	[ "Jiazhen Zhang", "Rajesh Venkataraman", "Lawrence H. Staib", "John A. Onofrey" ]	https://conferences.miccai.org/2022/papers/049-Paper2434.html	null	10.1007/978-3-031-16443-9_55	https://rdcu.be/cVRy9	null	[ "Image Segmentation", "Modalities - MRI" ]	[ "https://github.com/OnofreyLab/prostate_atlas_segm_miccai2022" ]	[]	null	null	Segmentation of the prostate into specific anatomical zones is important for radiological assessment of prostate cancer in magnetic resonance imaging (MRI). Of particular interest is segmenting the prostate into two regions of interest: the central gland (CG) and peripheral zone (PZ). In this paper, we propose to integ...	null	null
Paper0345	Atlas-powered deep learning (ADL) - application to diffusion weighted MRI	[ "Davood Karimi", "Ali Gholipour" ]	https://conferences.miccai.org/2022/papers/050-Paper0345.html	null	10.1007/978-3-031-16431-6_12	https://rdcu.be/cVD4T	null	[ "Organ - Brain", "Modalities - MRI" ]	[]	[]	null	null	Deep learning has a great potential for estimating biomarkers in diffusion weighted magnetic resonance imaging (dMRI). Atlases, on the other hand, are a unique tool for modeling the spatio-temporal variability of biomarkers. In this paper, we propose the first framework to exploit both deep learning and atlases for bio...	2205.03210	title_snapshot
Paper1798	Attention mechanisms for physiological signal deep learning: which attention should we take?	[ "Seong-A Park", "Hyung-Chul Lee", "Chul-Woo Jung", "Hyun-Lim Yang" ]	https://conferences.miccai.org/2022/papers/051-Paper1798.html	null	10.1007/978-3-031-16431-6_58	https://rdcu.be/cVD7e	null	[ "Machine Learning - Other", "Modalities - EEG/ECG" ]	[]	[ "https://vitaldb.net" ]	null	null	Attention mechanisms are widely used to dramatically improve deep learning model performance in various fields. However, their general ability to improve the performance of physiological signal deep learning model is immature. In this study, we experimentally analyze four attention mechanisms (e.g., squeeze-and-excitat...	2207.06904	title_snapshot
Paper2482	Attentional Generative Multimodal Network for Neonatal Postoperative Pain Estimation	[ "Md Sirajus Salekin", "Ghada Zamzmi", "Dmitry Goldgof", "Peter R. Mouton", "Kanwaljeet J. S. Anand", "Terri Ashmeade", "Stephanie Prescott", "Yangxin Huang", "Yu Sun" ]	https://conferences.miccai.org/2022/papers/052-Paper2482.html	null	10.1007/978-3-031-16437-8_72	https://rdcu.be/cVRuX	null	[ "Computer Aided Diagnosis", "Machine Learning - Semi-/Weakly-/Un-/Self-supervised representation learning", "Modalities - other", "Modalities - Video", "Outcome/disease prediction" ]	[]	[ "https://doi.org/10.1016/j.dib.2021.106796" ]	null	null	Artificial Intelligence (AI)-based methods allow for automatic assessment of pain intensity based on continuous monitoring and processing of subtle changes in sensory signals, including facial expression, body movements, and crying frequency. Currently, there is a large and growing need for expanding current AI-based a...	null	null
Paper1971	Attention-enhanced Disentangled Representation Learning for Unsupervised Domain Adaptation in Cardiac Segmentation	[ "Xiaoyi Sun", "Zhizhe Liu", "Shuai Zheng", "Chen Lin", "Zhenfeng Zhu", "Yao Zhao" ]	https://conferences.miccai.org/2022/papers/053-Paper1971.html	null	10.1007/978-3-031-16449-1_71	https://rdcu.be/cVRXJ	null	[ "Machine Learning - Transfer learning", "Image Segmentation", "Machine Learning - Semi-/Weakly-/Un-/Self-supervised representation learning", "Modalities - CT", "Modalities - MRI", "Organ - Heart" ]	[ "https://github.com/Sunxy11/ADR" ]	[]	null	null	To overcome the barriers of multimodality and scarcity of annotations in medical image segmentation, many unsupervised domain adaptation (UDA) methods have been proposed, especially in cardiac segmentation. However, these methods may not completely avoid the interference of domain-specific information. To tackle this p...	null	null
Paper0676	Attentive Symmetric Autoencoder for Brain MRI Segmentation	[ "Junjia Huang", "Haofeng Li", "Guanbin Li", "Xiang Wan" ]	https://conferences.miccai.org/2022/papers/054-Paper0676.html	null	10.1007/978-3-031-16443-9_20	https://rdcu.be/cVRyy	null	[ "Computer Aided Diagnosis", "Organ - Brain" ]	[]	[ "http://adni.loni.usc.edu/", "https://www.oasis-brains.org/", "http://www.braintumorsegmentation.org/", "https://www.nitrc.org/projects/ibsr/", "https://wmh.isi.uu.nl/data/" ]	null	null	Self-supervised learning methods based on image patch reconstruction have witnessed great success in training auto-encoders, whose pre-trained weights can be transferred to fine-tune other downstream tasks of image understanding. However, existing methods seldom study the various importance of reconstructed patches and...	2209.08887	title_snapshot
Paper0121	Autofocusing+: Noise-Resilient Motion Correction in Magnetic Resonance Imaging	[ "Ekaterina Kuzmina", "Artem Razumov", "Oleg Y. Rogov", "Elfar Adalsteinsson", "Jacob White", "Dmitry V. Dylov" ]	https://conferences.miccai.org/2022/papers/055-Paper0121.html	null	10.1007/978-3-031-16446-0_35	https://rdcu.be/cVRTu	null	[ "Image Reconstruction", "Image Registration", "Modalities - MRI", "Organ - Musculoskeleton" ]	[ "https://github.com/cviaai/AF-PLUS" ]	[]	null	null	Image corruption by motion artifacts is an ingrained problem in Magnetic Resonance Imaging (MRI). In this work, we propose a neural network-based regularization term to enhance Autofocusing, a classic optimization-based method to remove motion artifacts. The method takes the best of both worlds: the optimization-based ...	2203.05569	title_snapshot
Paper0386	AutoGAN-Synthesizer: Neural Architecture Search for Cross-Modality MRI Synthesis	[ "Xiaobin Hu", "Ruolin Shen", "Donghao Luo", "Ying Tai", "Chengjie Wang", "Bjoern H. Menze" ]	https://conferences.miccai.org/2022/papers/056-Paper0386.html	null	10.1007/978-3-031-16446-0_38	https://rdcu.be/cVRTx	null	[ "Image Reconstruction", "Modalities - MRI" ]	[]	[]	null	null	Considering the difficulty to obtain complete multi-modality MRI scans in some real-world data acquisition situations, synthesizing MRI data is a highly relevant and important topic to complement diagnosis information in clinical practice. In this study, we present a novel MRI synthesizer, called AutoGAN-Synthesizer, w...	null	null
Paper1988	AutoLaparo: A New Dataset of Integrated Multi-tasks for Image-guided Surgical Automation in Laparoscopic Hysterectomy	[ "Ziyi Wang", "Bo Lu", "Yonghao Long", "Fangxun Zhong", "Tak-Hong Cheung", "Qi Dou", "Yunhui Liu" ]	https://conferences.miccai.org/2022/papers/057-Paper1988.html	null	10.1007/978-3-031-16449-1_46	https://rdcu.be/cVRXl	null	[ "Image Analysis in Robot-Assisted Surgery", "Image-Guided Interventions", "Modalities - Endoscope", "Surgical Data Science", "Surgical Scene Understanding", "Surgical Skill and Work Flow Analysis" ]	[]	[ "https://autolaparo.github.io" ]	null	null	Computer-assisted minimally invasive surgery has great potential in benefiting modern operating theatres. The video data streamed from the endoscope provides rich information to support context-awareness for next-generation intelligent surgical systems. To achieve accurate perception and automatic manipulation during t...	2208.02049	title_snapshot
Paper1919	Automated Classification of General Movements in Infants Using Two-stream Spatiotemporal Fusion Network	[ "Yuki Hashimoto", "Akira Furui", "Koji Shimatani", "Maura Casadio", "Paolo Moretti", "Pietro Morasso", "Toshio Tsuji" ]	https://conferences.miccai.org/2022/papers/058-Paper1919.html	null	10.1007/978-3-031-16434-7_72	https://rdcu.be/cVRsE	null	[ "Computer Aided Diagnosis", "Image Segmentation", "Machine Learning - Other", "Modalities - Video" ]	[ "https://github.com/uoNuM/two-stream-gma" ]	[]	null	null	The assessment of general movements (GMs) in infants is a useful tool in the early diagnosis of neurodevelopmental disorders. However, its evaluation in clinical practice relies on visual inspection by experts, and an automated solution is eagerly awaited. Recently, video-based GMs classification has attracted attentio...	2207.03344	title_judge
Paper2247	Automatic Detection of Steatosis in Ultrasound Images with Comparative Visual Labeling	[ "Güinther Saibro", "Michele Diana", "Benoît Sauer", "Jacques Marescaux", "Alexandre Hostettler", "Toby Collins" ]	https://conferences.miccai.org/2022/papers/059-Paper2247.html	null	10.1007/978-3-031-16437-8_39	https://rdcu.be/cVRtp	null	[ "Computer Aided Diagnosis", "Machine Learning - Other", "Modalities - Ultrasound", "Organ - Abdomen" ]	[ "https://github.com/IRCAD/cvl" ]	[ "https://www.ircad.fr/research/data-sets/" ]	null	null	A common difficulty in computer-assisted diagnosis is acquiring accurate and representative labeled data, required to train, test and monitor models. Concerning liver steatosis detection in ultrasound (US) images, labeling images with human annotators can be error-prone because of subjectivity and decision boundary bia...	null	null
Paper1361	Automatic identification of segmentation errors for radiotherapy using geometric learning	[ "Edward G. A. Henderson", "Andrew F. Green", "Marcel van Herk", "Eliana M. Vasquez Osorio" ]	https://conferences.miccai.org/2022/papers/060-Paper1361.html	null	10.1007/978-3-031-16443-9_31	https://rdcu.be/cVRyL	null	[ "Image Segmentation", "Machine Learning - Data efficient Learning", "Machine Learning - Other", "Machine Learning - Semi-/Weakly-/Un-/Self-supervised representation learning", "Machine Learning - Transfer learning", "Modalities - CT", "Organ - Other" ]	[ "https://github.com/rrr-uom-projects/contour_auto_QATool" ]	[ "https://github.com/deepmind/tcia-ct-scan-dataset" ]	null	null	Automatic segmentation of organs-at-risk (OARs) in CT scans using convolutional neural networks (CNNs) is being introduced into the radiotherapy workflow. However, these segmentations still require manual editing and approval by clinicians prior to clinical use, which can be time consuming. The aim of this work was to ...	2206.13317	title_snapshot
Paper2252	Automatic Segmentation of Hip Osteophytes in DXA Scans using U-Nets	[ "Raja Ebsim", "Benjamin G. Faber", "Fiona Saunders", "Monika Frysz", "Jenny Gregory", "Nicholas C. Harvey", "Jonathan H. Tobias", "Claudia Lindner", "Timothy F. Cootes" ]	https://conferences.miccai.org/2022/papers/061-Paper2252.html	null	10.1007/978-3-031-16443-9_1	https://rdcu.be/cVRx7	null	[ "Image Segmentation", "Computer Aided Diagnosis", "Machine Learning - Other", "Organ - Musculoskeleton" ]	[]	[]	null	null	Osteophytes are distinctive radiographic features of osteoarthritis (OA) in the form of small bone spurs protruding from joints that contribute significantly to symptoms. Identifying the genetic determinants of osteophytes would improve the understanding of their biological pathways and contributions to OA. To date, th...	null	null
Paper0295	Automating Blastocyst Formation and Quality Prediction in Time-Lapse Imaging with Adaptive Key Frame Selection	[ "Tingting Chen", "Yi Cheng", "Jinhong Wang", "Zhaoxia Yang", "Wenhao Zheng", "Danny Z. Chen", "Jian Wu" ]	https://conferences.miccai.org/2022/papers/062-Paper0295.html	null	10.1007/978-3-031-16440-8_43	https://rdcu.be/cVRwu	null	[ "Modalities - Video", "Computer Aided Diagnosis" ]	[]	[]	null	null	Effective approaches for accurately predicting the developmental potential of embryos and selecting suitable embryos for blastocyst culture are critically needed. Many deep learning (DL) based methods for time-lapse monitoring (TLM) videos have been proposed to tackle this problem. Although fruitful, these methods are ...	null	null
Paper2315	Automation of clinical measurements on radiographs of children's hips	[ "Peter Thompson", "Medical Annotation Collaborative", "Daniel C. Perry", "Timothy F. Cootes", "Claudia Lindner" ]	https://conferences.miccai.org/2022/papers/063-Paper2315.html	null	10.1007/978-3-031-16437-8_40	https://rdcu.be/cVRtq	null	[ "Computer Aided Diagnosis", "Computational Anatomy and Physiology", "Organ - Musculoskeleton", "Outcome/disease prediction" ]	[]	[]	null	null	Developmental dysplasia of the hip (DDH) and cerebral palsy (CP) related hip migration are two of the most common orthopaedic diseases in children, each affecting around 1-2 in 1000 children. For both of these conditions, early detection is a key factor in long term outcomes for patients. However, early signs of the di...	null	null
Paper1731	BabyNet: Residual Transformer Module for Birth Weight Prediction on Fetal Ultrasound Video	[ "Szymon Płotka", "Michal K. Grzeszczyk", "Robert Brawura-Biskupski-Samaha", "Paweł Gutaj", "Michał Lipa", "Tomasz Trzciński", "Arkadiusz Sitek" ]	https://conferences.miccai.org/2022/papers/064-Paper1731.html	null	10.1007/978-3-031-16440-8_34	https://rdcu.be/cVRvZ	null	[ "Computer Aided Diagnosis", "Modalities - Ultrasound", "Modalities - Video", "Organ - Fetal", "Outcome/disease prediction" ]	[ "https://github.com/SanoScience/BabyNet" ]	[]	null	null	Predicting fetal weight at birth is an important aspect of perinatal care, particularly in the context of antenatal management, which includes the planned timing and the mode of delivery. Accurate prediction of weight using prenatal ultrasound is challenging as it requires images of specific fetal body parts during adv...	2205.09382	title_snapshot
Paper0280	Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery	[ "Jingwei Song", "Qiuchen Zhu", "Jianyu Lin", "Maani Ghaffari" ]	https://conferences.miccai.org/2022/papers/065-Paper0280.html	null	10.1007/978-3-031-16449-1_32	https://rdcu.be/cVRW7	null	[ "Image-Guided Interventions", "Image Registration", "Interventional Imaging Systems", "Interventional Simulation Systems" ]	[ "https://github.com/JingweiSong/BDIS.git" ]	[ "https://github.com/JingweiSong/BDIS.git" ]	null	null	This paper reports a CPU-level real-time stereo matching method for surgical images (10 Hz on 640*480 image with a single core of i5-9400). The proposed method is built on the fast LK algorithm, which estimates the disparity of the stereo images patch-wisely and in a coarse-to-fine manner. We propose a Bayesian framewo...	2106.07136	title_snapshot
Paper2505	Bayesian Pseudo Labels: Expectation Maximization for Robust and Efficient Semi-Supervised Segmentation	[ "Mou-Cheng Xu", "Yukun Zhou", "Chen Jin", "Marius de Groot", "Daniel C. Alexander", "Neil P. Oxtoby", "Yipeng Hu", "Joseph Jacob" ]	https://conferences.miccai.org/2022/papers/066-Paper2505.html	null	10.1007/978-3-031-16443-9_56	https://rdcu.be/cVRza	null	[ "Machine Learning - Semi-/Weakly-/Un-/Self-supervised representation learning", "Image Segmentation", "Modalities - CT", "Organ - Lung", "Organ - Vessel" ]	[ "https://github.com/moucheng2017/EMSSL" ]	[ "https://arteryvein.grand-challenge.org/Home/", "https://www.med.upenn.edu/sbia/brats2018/data.html" ]	null	null	This paper concerns pseudo labelling in segmentation. Our contribution is fourfold. Firstly, we present a new formulation of pseudo-labelling as an Expectation-Maximization (EM) algorithm for clear statistical interpretation. Secondly, we propose a semi-supervised medical image segmentation method purely based on the o...	2208.04435	title_snapshot
Paper1462	Benchmarking the Robustness of Deep Neural Networks to Common Corruptions in Digital Pathology	[ "Yunlong Zhang", "Yuxuan Sun", "Honglin Li", "Sunyi Zheng", "Chenglu Zhu", "Lin Yang" ]	https://conferences.miccai.org/2022/papers/067-Paper1462.html	null	10.1007/978-3-031-16434-7_24	https://rdcu.be/cVRrG	null	[ "Computational (Integrative) Pathology", "Machine Learning - Model Generalizability" ]	[ "https://github.com/superjamessyx/robustness_benchmark" ]	[ "https://patchcamelyon.grand-challenge.org/" ]	null	null	When designing a diagnostic model for a clinical application, it is crucial to guarantee the robustness of the model with respect to a wide range of image corruptions. Herein, an easy-to-use benchmark is established to evaluate how deep neural networks perform on corrupted pathology images. Specifically, corrupted imag...	2206.14973	title_snapshot
Paper2856	BERTHop: An Effective Vision-and-Language Model for Chest X-ray Disease Diagnosis	[ "Masoud Monajatipoor", "Mozhdeh Rouhsedaghat", "Liunian Harold Li", "C.-C. Jay Kuo", "Aichi Chien", "Kai-Wei Chang" ]	https://conferences.miccai.org/2022/papers/068-Paper2856.html	null	10.1007/978-3-031-16443-9_69	https://rdcu.be/cVRzn	null	[ "Computer Aided Diagnosis", "Machine Learning - Other", "Machine Learning - Transfer learning", "Modalities - other", "Modalities - Text (clinical/radiology reports)", "Organ - Lung" ]	[ "https://github.com/monajati/BERTHop" ]	[ "https://openi.nlm.nih.gov/" ]	null	null	Vision-and-language (V&L) models take image and text as input and learn to capture the associations between them. These models can potentially deal with the tasks that involve understanding medical images along with their associated text. However, applying V&L models in the medical domain is challenging due to the expe...	2108.04938	title_snapshot
Paper1459	Bi-directional Encoding for Explicit Centerline Segmentation by Fully-Convolutional Networks	[ "Ilyas Sirazitdinov", "Axel Saalbach", "Heinrich Schulz", "Dmitry V. Dylov" ]	https://conferences.miccai.org/2022/papers/069-Paper1459.html	null	10.1007/978-3-031-16440-8_66	https://rdcu.be/cVRwT	null	[ "Image Segmentation", "Computer Aided Diagnosis", "Image-Guided Interventions", "Interventional Imaging Systems", "Modalities - other", "Organ - Heart", "Organ - Lung", "Organ - Vessel" ]	[]	[ "https://www.kaggle.com/competitions/ranzcr-clip-catheter-line-classification" ]	null	null	Localization of tube-shaped objects is an important topic in medical imaging. Previously it was mainly addressed via dense segmentation that may produce inconsistent results for long and narrow objects. In our work, we propose a point-based approach for explicit centerline segmentation that can be learned by fully-conv...	null	null
Paper1108	BiometryNet: Landmark-based Fetal Biometry Estimation from Standard Ultrasound Planes	[ "Netanell Avisdris", "Leo Joskowicz", "Brian Dromey", "Anna L. David", "Donald M. Peebles", "Danail Stoyanov", "Dafna Ben Bashat", "Sophia Bano" ]	https://conferences.miccai.org/2022/papers/070-Paper1108.html	null	10.1007/978-3-031-16440-8_27	https://rdcu.be/cVRvS	null	[ "Computer Aided Diagnosis", "Machine Learning - Model Generalizability", "Modalities - Ultrasound", "Organ - Fetal" ]	[ "https://github.com/netanellavisdris/fetalbiometry" ]	[ "https://doi.org/10.5281/zenodo.3904280", "https://zenodo.org/record/1327317" ]	null	null	Fetal growth assessment from ultrasound is based on a few biometric measurements that are performed manually and assessed relative to the expected gestational age. Reliable biometry estimation depends on the precise detection of landmarks in standard ultrasound planes. Manual annotation can be a time-consuming and oper...	2206.14678	title_snapshot
Paper1852	BMD-GAN: Bone mineral density estimation using x-ray image decomposition into projections of bone-segmented quantitative computed tomography using hierarchical learning	[ "Yi Gu", "Yoshito Otake", "Keisuke Uemura", "Mazen Soufi", "Masaki Takao", "Nobuhiko Sugano", "Yoshinobu Sato" ]	https://conferences.miccai.org/2022/papers/071-Paper1852.html	null	10.1007/978-3-031-16446-0_61	https://rdcu.be/cVRT5	null	[ "Image Reconstruction", "Computer Aided Diagnosis", "Modalities - other", "Organ - Musculoskeleton" ]	[ "https://github.com/NAIST-ICB/BMD-GAN" ]	[]	null	null	We propose a method for estimating the bone mineral density (BMD) from a plain x-ray image. Dual-energy X-ray absorptiometry (DXA) and quantitative computed tomography (QCT) provide high accuracy in diagnosing osteoporosis; however, these modalities require special equipment and scan protocols. Measuring BMD from an x-...	2207.03210	title_snapshot
Paper0820	Boundary-Enhanced Self-Supervised Learning for Brain Structure Segmentation	[ "Feng Chang", "Chaoyi Wu", "Yanfeng Wang", "Ya Zhang", "Xin Chen", "Qi Tian" ]	https://conferences.miccai.org/2022/papers/072-Paper0820.html	null	10.1007/978-3-031-16431-6_2	https://rdcu.be/cVD4J	null	[ "Machine Learning - Semi-/Weakly-/Un-/Self-supervised representation learning", "Image Registration", "Organ - Brain" ]	[ "https://github.com/changfeng3168/BE-SSL" ]	[]	null	null	To alleviate the demand for large amount of annotated data by deep learning methods, this paper explores self-supervised learning (SSL) for brain structure segmentation. Most SSL methods treat all pixels equally, failing to emphasize the boundaries that are important clues for segmentation. We propose Boundary-Enhanced...	null	null
Paper0016	BoxPolyp: Boost Generalized Polyp Segmentation using Extra Coarse Bounding Box Annotations	[ "Jun Wei", "Yiwen Hu", "Guanbin Li", "Shuguang Cui", "S. Kevin Zhou", "Zhen Li" ]	https://conferences.miccai.org/2022/papers/073-Paper0016.html	null	10.1007/978-3-031-16437-8_7	https://rdcu.be/cVRsT	null	[ "Image Segmentation", "Modalities - Endoscope" ]	[]	[]	null	null	Accurate polyp segmentation is of great importance for colorectal cancer diagnosis and treatment. However, due to the high cost of producing accurate mask annotations, existing polyp segmentation methods suffer from severe data shortage and impaired model generalization. Reversely, coarse polyp bounding box annotations...	2212.03498	title_snapshot
Paper2706	Brain-Aware Replacements for Supervised Contrastive Learning in Detection of Alzheimer’s Disease	[ "Mehmet Saygın Seyfioğlu", "Zixuan Liu", "Pranav Kamath", "Sadjyot Gangolli", "Sheng Wang", "Thomas Grabowski", "Linda Shapiro" ]	https://conferences.miccai.org/2022/papers/074-Paper2706.html	null	10.1007/978-3-031-16431-6_44	https://rdcu.be/cVD60	null	[ "Machine Learning - Semi-/Weakly-/Un-/Self-supervised representation learning", "Machine Learning - Data efficient Learning", "Machine Learning - Interpretability / Explainability", "Modalities - MRI", "Organ - Brain" ]	[ "https://github.com/aldraus/BrainAwareReplacementsForAD" ]	[ "https://ida.loni.usc.edu/login.jsp?project=ADNI&page=HOME" ]	null	null	We propose a novel framework for Alzheimer’s disease (AD) detection using brain MRIs. The framework starts with a data augmentation method called Brain-Aware Replacements (BAR), which leverages a standard brain parcellation to replace medically-relevant 3D brain regions in an anchor MRI from a randomly picked MRI to cr...	2207.04574	title_snapshot
Paper2270	Breaking with Fixed Set Pathology Recognition through Report-Guided Contrastive Training	[ "Constantin Seibold", "Simon Reiß", "M. Saquib Sarfraz", "Rainer Stiefelhagen", "Jens Kleesiek" ]	https://conferences.miccai.org/2022/papers/075-Paper2270.html	null	10.1007/978-3-031-16443-9_66	https://rdcu.be/cVRzk	null	[ "Modalities - Text (clinical/radiology reports)", "Machine Learning - Model Generalizability", "Machine Learning - Semi-/Weakly-/Un-/Self-supervised representation learning", "Machine Learning - Transfer learning" ]	[]	[]	null	null	When reading images, radiologists generate text reports describing the findings therein. Current state-of-the-art computer-aided diagnosis tools utilize a fixed set of predefined categories automatically extracted from these medical reports for training. This form of supervision limits the potential usage of models as ...	2205.07139	title_snapshot
Paper1095	Building Brains: Subvolume Recombination for Data Augmentation in Large Vessel Occlusion Detection	[ "Florian Thamm", "Oliver Taubmann", "Markus Jürgens", "Aleksandra Thamm", "Felix Denzinger", "Leonhard Rist", "Hendrik Ditt", "Andreas Maier" ]	https://conferences.miccai.org/2022/papers/076-Paper1095.html	null	10.1007/978-3-031-16437-8_61	https://rdcu.be/cVRuM	null	[ "Computer Aided Diagnosis", "Modalities - CT", "Organ - Brain" ]	[]	[]	null	null	Ischemic strokes are often caused by large vessel occlusions (LVOs), which can be visualized and diagnosed with Computed Tomography Angiography scans. As time is brain, a fast, accurate and automated diagnosis of these scans is desirable. Human readers compare the left and right hemispheres in their assessment of strok...	2205.02848	title_snapshot
Paper0239	CACTUSS: Common Anatomical CT-US Space for US examinations	[ "Yordanka Velikova", "Walter Simson", "Mehrdad Salehi", "Mohammad Farid Azampour", "Philipp Paprottka", "Nassir Navab" ]	https://conferences.miccai.org/2022/papers/077-Paper0239.html	null	10.1007/978-3-031-16437-8_47	https://rdcu.be/cVRuy	null	[ "Image-Guided Interventions", "Image Segmentation", "Machine Learning - Transfer learning", "Modalities - CT", "Organ - Abdomen", "Organ - Vessel" ]	[ "https://github.com/danivelikova/cactuss" ]	[]	null	null	Abdominal aortic aneurysm (AAA) is a vascular disease in which a section of the aorta enlarges, weakening its walls and potentially rupturing the vessel. Abdominal ultrasound has been utilized for diagnostics, but due to its limited image quality and operator dependency, CT scans are usually required for monitoring and...	2207.08619	title_snapshot
Paper0017	Calibrating Label Distribution for Class-Imbalanced Barely-Supervised Knee Segmentation	[ "Yiqun Lin", "Huifeng Yao", "Zezhong Li", "Guoyan Zheng", "Xiaomeng Li" ]	https://conferences.miccai.org/2022/papers/078-Paper0017.html	null	10.1007/978-3-031-16452-1_11	https://rdcu.be/cVRYO	null	[ "Machine Learning - Semi-/Weakly-/Un-/Self-supervised representation learning", "Modalities - MRI", "Organ - Musculoskeleton" ]	[ "https://github.com/xmed-lab/CLD-Semi" ]	[]	null	null	Segmentation of 3D knee MR images is important for the assessment of osteoarthritis. Like other medical data, the volume-wise labeling of knee MR images is expertise-demanded and time-consuming; hence semi-supervised learning (SSL), particularly barely-supervised learning, is highly desirable for training with insuffic...	2205.03644	title_snapshot
Paper0963	Calibration of Medical Imaging Classification Systems with Weight Scaling	[ "Lior Frenkel", "Jacob Goldberger" ]	https://conferences.miccai.org/2022/papers/079-Paper0963.html	null	10.1007/978-3-031-16452-1_61	https://rdcu.be/cVVqi	null	[ "Machine Learning - Other", "Machine Learning - Interpretability / Explainability", "Organ - Breast" ]	[]	[]	null	null	Calibrating neural networks is crucial in medical analysis applications where the decision making depends on the predicted probabilities. Modern neural networks are not well calibrated and they tend to overestimate probabilities when compared to the expected accuracy. This results in a misleading reliability that corru...	null	null
Paper1145	Camera Adaptation for Fundus-Image-Based CVD Risk Estimation	[ "Zhihong Lin", "Danli Shi", "Donghao Zhang", "Xianwen Shang", "Mingguang He", "Zongyuan Ge" ]	https://conferences.miccai.org/2022/papers/080-Paper1145.html	null	10.1007/978-3-031-16434-7_57	https://rdcu.be/cVRsp	null	[ "Organ - Eye", "Machine Learning - Model Generalizability", "Outcome/disease prediction" ]	[ "https://github.com/linzhlalala/CVD-risk-based-on-retinal-fundus-images" ]	[]	null	null	Recent studies have validated the association between cardiovascular disease (CVD) risk and retinal fundus images. Combining deep learning (DL) and portable fundus cameras will enable CVD risk estimation in various scenarios and improve healthcare democratization. However, there are still significant issues to be solve...	2206.09202	title_snapshot
Paper2332	Capturing Shape Information with Multi-Scale Topological Loss Terms for 3D Reconstruction	[ "Dominik J. E. Waibel", "Scott Atwell", "Matthias Meier", "Carsten Marr", "Bastian Rieck" ]	https://conferences.miccai.org/2022/papers/081-Paper2332.html	null	10.1007/978-3-031-16440-8_15	https://rdcu.be/cVRvF	null	[ "Image Reconstruction", "Image Segmentation", "Machine Learning - Other", "Modalities - Microscopy" ]	[ "https://github.com/marrlab/SHAPR_torch" ]	[]	null	null	Reconstructing 3D objects from 2D images is both challenging for our brains and machine learning algorithms. To support this spatial reasoning task, contextual information about the overall shape of an object is critical. However, such information is not captured by established loss terms (e.g. Dice loss). We propose t...	2203.01703	title_snapshot
Paper2156	Carbon Footprint of Selecting and Training Deep Learning Models for Medical Image Analysis	[ "Raghavendra Selvan", "Nikhil Bhagwat", "Lasse F. Wolff Anthony", "Benjamin Kanding", "Erik B. Dam" ]	https://conferences.miccai.org/2022/papers/082-Paper2156.html	null	10.1007/978-3-031-16443-9_49	https://rdcu.be/cVRy3	null	[ "Machine Learning - Other", "Image Segmentation", "Machine Learning - Data efficient Learning" ]	[]	[]	null	null	The increasing energy consumption and carbon footprint of deep learning (DL) due to growing compute requirements has become a cause of concern. In this work, we focus on the carbon footprint of developing DL models for medical image analysis (MIA), where volumetric images of high spatial resolution are handled. In this...	2203.02202	title_snapshot
Paper0783	CaRTS: Causality-driven Robot Tool Segmentation from Vision and Kinematics Data	[ "Hao Ding", "Jintan Zhang", "Peter Kazanzides", "Jie Ying Wu", "Mathias Unberath" ]	https://conferences.miccai.org/2022/papers/083-Paper0783.html	null	10.1007/978-3-031-16449-1_37	https://rdcu.be/cVRXc	null	[ "Image Analysis in Robot-Assisted Surgery", "Image Segmentation" ]	[ "https://github.com/hding2455/CaRTS" ]	[]	null	null	Vision-based segmentation of the robotic tool during robot-assisted surgery enables downstream applications, such as augmented reality feedback, while allowing for inaccuracies in robot kinematics. With the introduction of deep learning, many methods were presented to solve instrument segmentation directly and solely f...	2203.09475	title_snapshot
Paper1369	CASHformer: Cognition Aware SHape Transformer for Longitudinal Analysis	[ "Ignacio Sarasua", "Sebastian Pölsterl", "Christian Wachinger" ]	https://conferences.miccai.org/2022/papers/084-Paper1369.html	null	10.1007/978-3-031-16431-6_5	https://rdcu.be/cVD4M	null	[ "Machine Learning - Other", "Computer Aided Diagnosis", "Machine Learning - Model Generalizability", "Machine Learning - Transfer learning", "Modalities - MRI", "Organ - Brain" ]	[ "https://github.com/ai-med" ]	[]	null	null	Modeling temporal changes in subcortical structures is crucial for a better understanding of the progression of Alzheimer’s disease (AD). Given their flexibility to adapt to heterogeneous sequence lengths, mesh-based transformer architectures have been proposed in the past for predicting hippocampus deformations across...	2207.02091	title_snapshot
Paper0894	Censor-aware Semi-supervised Learning for Survival Time Prediction from Medical Images	[ "Renato Hermoza", "Gabriel Maicas", "Jacinto C. Nascimento", "Gustavo Carneiro" ]	https://conferences.miccai.org/2022/papers/085-Paper0894.html	null	10.1007/978-3-031-16449-1_21	https://rdcu.be/cVRU1	null	[ "Outcome/disease prediction", "Machine Learning - Semi-/Weakly-/Un-/Self-supervised representation learning", "Modalities - CT", "Modalities - Histopathology" ]	[ "https://github.com/renato145/CASurv" ]	[]	null	null	Survival time prediction from medical images is important for treatment planning, where accurate estimations can improve healthcare quality. One issue affecting the training of survival models is censored data. Most of the current survival prediction approaches are based on Cox models that can deal with censored data, ...	2205.13226	title_snapshot
Paper0497	CephalFormer: Incorporating Global Structure Constraint into Visual Features for General Cephalometric Landmark Detection	[ "Yankai Jiang", "Yiming Li", "Xinyue Wang", "Yubo Tao", "Jun Lin", "Hai Lin" ]	https://conferences.miccai.org/2022/papers/086-Paper0497.html	null	10.1007/978-3-031-16437-8_22	https://rdcu.be/cVRs8	null	[ "Computer Aided Diagnosis", "Modalities - CT", "Organ - Musculoskeleton" ]	[]	[]	null	null	Accurate cephalometric landmark detection is a crucial step in orthodontic diagnosis and therapy planning. However, existing deep learning-based methods lack the ability to explicitly model the complex dependencies among visual features and landmarks. Therefore, they fail to adaptively encode the landmark’s global stru...	null	null
Paper2197	Cerebral Microbleeds Detection Using a 3D Feature Fused Region Proposal Network with Hard Sample Prototype Learning	[ "Jun-Ho Kim", "Mohammed A. Al-masni", "Seul Lee", "Haejoon Lee", "Dong-Hyun Kim" ]	https://conferences.miccai.org/2022/papers/087-Paper2197.html	null	10.1007/978-3-031-16431-6_43	https://rdcu.be/cVD6Z	null	[ "Computer Aided Diagnosis", "Machine Learning - Other", "Modalities - MRI", "Organ - Brain", "Organ - Vessel", "Outcome/disease prediction" ]	[]	[]	null	null	Cerebral Microbleeds (CMBs) are chronic deposits of small blood products in the brain tissues, which have explicit relation to cerebrovascular diseases, including cognitive decline, intracerebral hemorrhage, and cerebral infarction. However, manual detection of the CMBs is a time-consuming and error-prone process be-ca...	null	null
Paper0402	CFDA: Collaborative Feature Disentanglement and Augmentation for Pulmonary Airway Tree Modeling of COVID-19 CTs	[ "Minghui Zhang", "Hanxiao Zhang", "Guang-Zhong Yang", "Yun Gu" ]	https://conferences.miccai.org/2022/papers/088-Paper0402.html	null	10.1007/978-3-031-16431-6_48	https://rdcu.be/cVD64	null	[ "Machine Learning - Transfer learning", "Modalities - CT", "Organ - Lung" ]	[ "https://github.com/Puzzled-Hui/CFDA" ]	[ "http://www.pami.sjtu.edu.cn/Show/56/126" ]	null	null	Detailed modeling of the airway tree from CT scan is important for 3D navigation involved in endobronchial intervention including for those patients infected with the novel coronavirus. Deep learning methods have the potential for automatic airway segmentation but require large annotated datasets for training, which is...	null	null
Paper1601	Characterization of brain activity patterns across states of consciousness based on variational auto-encoders	[ "Chloé Gomez", "Antoine Grigis", "Lynn Uhrig", "Béchir Jarraya" ]	https://conferences.miccai.org/2022/papers/089-Paper1601.html	null	10.1007/978-3-031-16431-6_40	https://rdcu.be/cVD6W	null	[ "Machine Learning - Semi-/Weakly-/Un-/Self-supervised representation learning", "Computer Aided Diagnosis", "Modalities - MRI", "Organ - Brain", "Visualisation in Biomedical Imaging" ]	[]	[]	null	null	Decoding the levels of consciousness from cortical activity recording is a major challenge in neuroscience. The spontaneous fluctuations of brain activity through different patterns across time are monitored using resting-state functional MRI. The different dynamic functional configurations of the brain during resting-...	null	null
Paper1593	CheXRelNet: An Anatomy-Aware Model for Tracking Longitudinal Relationships between Chest X-Rays	[ "Gaurang Karwande", "Amarachi B. Mbakwe", "Joy T. Wu", "Leo A. Celi", "Mehdi Moradi", "Ismini Lourentzou" ]	https://conferences.miccai.org/2022/papers/090-Paper1593.html	null	10.1007/978-3-031-16431-6_55	https://rdcu.be/cVD7b	null	[ "Computer Aided Diagnosis", "Machine Learning - Other", "Outcome/disease prediction" ]	[ "https://github.com/PLAN-Lab/ChexRelNet" ]	[ "https://physionet.org/content/mimic-cxr-jpg/2.0.0/", "https://physionet.org/content/chest-imagenome/1.0.0/" ]	null	null	Despite the progress in utilizing deep learning to automate chest radiograph interpretation and disease diagnosis tasks, change between sequential Chest X-rays (CXRs) has received limited attention. Monitoring the progression of pathologies that are visualized through chest imaging poses several challenges in anatomica...	2208.03873	title_snapshot
Paper1588	ChrSNet: Chromosome Straightening using Self-attention Guided Networks	[ "Sunyi Zheng", "Jingxiong Li", "Zhongyi Shui", "Chenglu Zhu", "Yunlong Zhang", "Pingyi Chen", "Lin Yang" ]	https://conferences.miccai.org/2022/papers/091-Paper1588.html	null	10.1007/978-3-031-16440-8_12	https://rdcu.be/cVRvw	null	[ "Modalities - Microscopy", "Computer Aided Diagnosis", "Machine Learning - Other" ]	[ "https://github.com/lijx1996/ChrSNet" ]	[]	null	null	Karyotyping is an important procedure to assess the possible existence of chromosomal abnormalities. However, because of the non-rigid nature, chromosomes are usually heavily curved in microscopic images and such deformed shapes hinder the chromosome analysis for cytogeneticists. In this paper, we present a self-attent...	2207.00147	title_snapshot
Paper2297	CIRDataset: A large-scale Dataset for Clinically-Interpretable lung nodule Radiomics and malignancy prediction	[ "Wookjin Choi", "Navdeep Dahiya", "Saad Nadeem" ]	https://conferences.miccai.org/2022/papers/092-Paper2297.html	null	10.1007/978-3-031-16443-9_2	https://rdcu.be/cVRx8	null	[ "Computational Anatomy and Physiology", "Image Segmentation", "Modalities - CT", "Organ - Abdomen", "Visualisation in Biomedical Imaging" ]	[ "https://github.com/nadeemlab/CIR" ]	[ "https://zenodo.org/record/6762573" ]	null	null	Spiculations/lobulations, sharp/curved spikes on the surface of lung nodules, are good predictors of lung cancer malignancy and hence, are routinely assessed and reported by radiologists as part of the standardized Lung-RADS clinical scoring criteria. Given the 3D geometry of the nodule and 2D slice-by-slice assessment...	2206.14903	title_snapshot
Paper1320	Class Impression for Data-free Incremental Learning	[ "Sana Ayromlou", "Purang Abolmaesumi", "Teresa Tsang", "Xiaoxiao Li" ]	https://conferences.miccai.org/2022/papers/093-Paper1320.html	null	10.1007/978-3-031-16440-8_31	https://rdcu.be/cVRvW	null	[ "Machine Learning - Continual Learning", "Machine Learning - Model Generalizability", "Machine Learning - Other", "Modalities - Ultrasound", "Organ - Heart" ]	[ "https://github.com/sanaAyrml/Class-Impresion-for-Data-free-Incremental-Learning.git" ]	[]	null	null	Standard deep learning-based classification approaches require collecting all samples from all classes in advance and are trained offline. This paradigm may not be practical in real-world clinical applications, where new classes are incrementally introduced through the addition of new data. Class incremental learning i...	2207.00005	title_snapshot
Paper1363	Classification-aided High-quality PET Image Synthesis via Bidirectional Contrastive GAN with Shared Information Maximization	[ "Yuchen Fei", "Chen Zu", "Zhengyang Jiao", "Xi Wu", "Jiliu Zhou", "Dinggang Shen", "Yan Wang" ]	https://conferences.miccai.org/2022/papers/094-Paper1363.html	null	10.1007/978-3-031-16446-0_50	https://rdcu.be/cVRTJ	null	[ "Image Reconstruction", "Modalities - PET/SPECT", "Organ - Brain" ]	[]	[]	null	null	Positron emission tomography (PET) is a pervasively adopted nuclear imaging technique, however, its inherent tracer radiation inevitably causes potential health hazards to patients. To obtain high-quality PET image while reducing radiation exposure, this paper proposes an algorithm for high-quality standard-dose PET (S...	null	null
Paper0431	Clinical-realistic Annotation for Histopathology Images with Probabilistic Semi-supervision: A Worst-case Study	[ "Ziyue Xu", "Andriy Myronenko", "Dong Yang", "Holger R. Roth", "Can Zhao", "Xiaosong Wang", "Daguang Xu" ]	https://conferences.miccai.org/2022/papers/095-Paper0431.html	null	10.1007/978-3-031-16434-7_8	https://rdcu.be/cVRq6	null	[ "Machine Learning - Data efficient Learning", "Computer Aided Diagnosis", "Machine Learning - Semi-/Weakly-/Un-/Self-supervised representation learning", "Machine Learning - Uncertainty", "Modalities - Histopathology" ]	[]	[ "https://camelyon16.grand-challenge.org/Data/" ]	null	null	Acquiring pixel-level annotation has been a major challenge for machine learning methods in medical image analysis. Such difficulty mainly comes from two sources: localization requiring high expertise, and delineation requiring tedious and time-consuming work. Existing methods of easing the annotation effort mostly foc...	null	null

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Accepted papers for MICCAI (Medical Image Computing and Computer Assisted Intervention), one dataset per year. • 4 items • Updated 2 days ago