title stringlengths 2 287 | abstract stringlengths 0 5.14k ⌀ | journal stringlengths 4 184 | date timestamp[s] | authors listlengths 1 57 | doi stringlengths 16 6.63k ⌀ |
|---|---|---|---|---|---|
A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images. | To utilize a deep learning model for automatic detection of abnormalities in chest CT images from COVID-19 patients and compare its quantitative determination performance with radiological residents.
A deep learning algorithm consisted of lesion detection, segmentation, and location was trained and validated in 14,435 ... | European radiology | 2020-07-04T00:00:00 | [
"QianqianNi",
"Zhi YuanSun",
"LiQi",
"WenChen",
"YiYang",
"LiWang",
"XinyuanZhang",
"LiuYang",
"YiFang",
"ZijianXing",
"ZhenZhou",
"YizhouYu",
"Guang MingLu",
"Long JiangZhang"
] | 10.1007/s00330-020-07044-9
10.1136/bmj.m406
10.1056/NEJMoa2001017
10.1136/bmj.m641
10.1016/j.meegid.2020.104211
10.1007/s10916-020-1536-6
10.1038/s41591-018-0300-7
10.1038/s41586-019-1390-1
10.1101/2020.02.14.20023028v2
10.7150/thno.46465
10.1038/nature14539
10.1148/radiol.2018180237
10.1371/journal.pmed.1002686 |
Development of a clinical decision support system for severity risk prediction and triage of COVID-19 patients at hospital admission: an international multicentre study. | The outbreak of coronavirus disease 2019 (COVID-19) has globally strained medical resources and caused significant mortality.
To develop and validate a machine-learning model based on clinical features for severity risk assessment and triage for COVID-19 patients at hospital admission.
725 patients were used to train a... | The European respiratory journal | 2020-07-04T00:00:00 | [
"GuangyaoWu",
"PeiYang",
"YuanliangXie",
"Henry CWoodruff",
"XiangangRao",
"JulienGuiot",
"Anne-NoelleFrix",
"RenaudLouis",
"MichelMoutschen",
"JiaweiLi",
"JingLi",
"ChenggongYan",
"DanDu",
"ShengchaoZhao",
"YiDing",
"BinLiu",
"WenwuSun",
"FabrizioAlbarello",
"AlessandraD'Abramo"... | 10.1183/13993003.01104-2020
10.1001/jama.2020.3204
10.1056/NEJMoa2002032
10.1097/CCM.0000000000004411
10.1056/NEJM199701233360402
10.1056/NEJMc1906060
10.1038/nrclinonc.2017.141
10.1016/S0140-6736(20)30260-9
10.1373/49.1.1
10.1164/rccm.201908-1581ST
10.18637/jss.v036.i11
10.2307/2531595
10.1016/S0140-6736(20)30183-5
10... |
Chest CT Evaluation of 11 Persistent Asymptomatic Patients with SARS-CoV-2 Infection. | In total, 11 asymptomatic carriers who underwent nasal or oropharyngeal swab tests for SARS-CoV-2 after being in close contact with patients who developed symptomatic 2019 coronavirus disease (COVID-19) were enrolled in this study. The chest multidetector computed tomography (CT) images of the enrolled patients were qu... | Japanese journal of infectious diseases | 2020-07-03T00:00:00 | [
"ShuoYan",
"HuiChen",
"Ru-MingXie",
"Chun-ShuangGuan",
"MingXue",
"Zhi-BinLv",
"Lian-GuiWei",
"YanBai",
"Bu-DongChen"
] | 10.7883/yoken.JJID.2020.264 |
Setting up an Easy-to-Use Machine Learning Pipeline for Medical Decision Support: A Case Study for COVID-19 Diagnosis Based on Deep Learning with CT Scans. | Coronavirus disease (COVID-19) constitutes an ongoing global health problem with significant morbidity and mortality. It usually presents characteristic findings on a chest CT scan, which may lead to early detection of the disease. A timely and accurate diagnosis of COVID-19 is the cornerstone for the prompt management... | Studies in health technology and informatics | 2020-07-02T00:00:00 | [
"AikateriniSakagianni",
"GeorgiosFeretzakis",
"DimitrisKalles",
"ChristinaKoufopoulou",
"VasileiosKaldis"
] | 10.3233/SHTI200481 |
Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays. | Coronavirus disease (COVID-19) is an infectious disease caused by a new virus never identified before in humans. This virus causes respiratory disease (for instance, flu) with symptoms such as cough, fever and, in severe cases, pneumonia. The test to detect the presence of this virus in humans is performed on sputum or... | Computer methods and programs in biomedicine | 2020-07-01T00:00:00 | [
"LucaBrunese",
"FrancescoMercaldo",
"AlfonsoReginelli",
"AntonellaSantone"
] | 10.1016/j.cmpb.2020.105608 |
Truncated inception net: COVID-19 outbreak screening using chest X-rays. | Since December 2019, the Coronavirus Disease (COVID-19) pandemic has caused world-wide turmoil in a short period of time, and the infection, caused by SARS-CoV-2, is spreading rapidly. AI-driven tools are used to identify Coronavirus outbreaks as well as forecast their nature of spread, where imaging techniques are wid... | Physical and engineering sciences in medicine | 2020-06-27T00:00:00 | [
"DipayanDas",
"K CSantosh",
"UmapadaPal"
] | 10.1007/s13246-020-00888-x
10.1016/j.acra.2020.03.003
10.1148/radiol.2020200432
10.1016/S0140-6736(20)30183-5
10.1007/s10916-020-01562-1
10.1007/s11548-016-1359-6
10.1007/s11548-015-1242-x
10.1007/s10916-018-0991-9
10.1109/TMI.2017.2775636 |
COVID-19 Pneumonia Diagnosis Using a Simple 2D Deep Learning Framework With a Single Chest CT Image: Model Development and Validation. | Coronavirus disease (COVID-19) has spread explosively worldwide since the beginning of 2020. According to a multinational consensus statement from the Fleischner Society, computed tomography (CT) is a relevant screening tool due to its higher sensitivity for detecting early pneumonic changes. However, physicians are ex... | Journal of medical Internet research | 2020-06-23T00:00:00 | [
"HoonKo",
"HeewonChung",
"Wu SeongKang",
"Kyung WonKim",
"YoungbinShin",
"Seung JiKang",
"Jae HoonLee",
"Young JunKim",
"Nan YeolKim",
"HyunseokJung",
"JinseokLee"
] | 10.2196/19569
10.1056/NEJMoa2004500
10.1080/22221751.2020.1745095
10.1080/22221751.2020.1745095
10.1002/jmv.25786
10.1148/radiol.2020200642
10.1148/radiol.2020201365
10.3390/diagnostics10040202
10.3348/kjr.2020.0146
10.1016/j.ejrad.2020.108961
10.1148/radiol.2020201326
10.1148/radiol.2020200823
10.1148/radiol.202020090... |
COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches. | Coronavirus causes a wide variety of respiratory infections and it is an RNA-type virus that can infect both humans and animal species. It often causes pneumonia in humans. Artificial intelligence models have been helpful for successful analyses in the biomedical field. In this study, Coronavirus was detected using a d... | Computers in biology and medicine | 2020-06-23T00:00:00 | [
"MesutToğaçar",
"BurhanErgen",
"ZaferCömert"
] | 10.1016/j.compbiomed.2020.103805
10.1056/nejmc2001468
10.1016/S0140-6736(20)30522-5
10.1136/bmj.m800
10.1038/s41368-020-0075-9
10.1016/j.mehy.2019.109503
10.1007/s10462-018-9641-3
10.1016/j.measurement.2019.05.076
10.1038/s41598-019-42294-8
10.1186/s40537-019-0276-2
10.1155/2018/4168538
10.1155/2019/4180949
10.3390/app... |
Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks. | Fast diagnostic methods can control and prevent the spread of pandemic diseases like coronavirus disease 2019 (COVID-19) and assist physicians to better manage patients in high workload conditions. Although a laboratory test is the current routine diagnostic tool, it is time-consuming, imposing a high cost and requirin... | Computers in biology and medicine | 2020-06-23T00:00:00 | [
"Ali AbbasianArdakani",
"Alireza RajabzadehKanafi",
"U RajendraAcharya",
"NazaninKhadem",
"AfshinMohammadi"
] | 10.1016/j.compbiomed.2020.103795
10.1148/radiol.2020200527
10.1148/radiol.2020200823
10.1016/S0140-6736(20)30673-5 |
Automated detection of COVID-19 cases using deep neural networks with X-ray images. | The novel coronavirus 2019 (COVID-2019), which first appeared in Wuhan city of China in December 2019, spread rapidly around the world and became a pandemic. It has caused a devastating effect on both daily lives, public health, and the global economy. It is critical to detect the positive cases as early as possible so... | Computers in biology and medicine | 2020-06-23T00:00:00 | [
"TulinOzturk",
"MuhammedTalo",
"Eylul AzraYildirim",
"Ulas BaranBaloglu",
"OzalYildirim",
"URajendra Acharya"
] | 10.1016/j.compbiomed.2020.103792
10.1148/radiol.2020200490
10.1148/radiol.2020200527
10.1148/radiol.2020200343
10.1148/radiol.2020200463
10.1148/radiol.2020200370
10.1038/nature21056
10.1101/2020.03.12.20027185
10.1148/radiol.2020200823 |
End-to-end automatic differentiation of the coronavirus disease 2019 (COVID-19) from viral pneumonia based on chest CT. | In the absence of a virus nucleic acid real-time reverse transcriptase-polymerase chain reaction (RT-PCR) test and experienced radiologists, clinical diagnosis is challenging for viral pneumonia with clinical symptoms and CT signs similar to that of coronavirus disease 2019 (COVID-19). We developed an end-to-end automa... | European journal of nuclear medicine and molecular imaging | 2020-06-23T00:00:00 | [
"JiangdianSong",
"HongmeiWang",
"YuchanLiu",
"WenqingWu",
"GangDai",
"ZongshanWu",
"PuheZhu",
"WeiZhang",
"Kristen WYeom",
"KexueDeng"
] | 10.1007/s00259-020-04929-1
10.1016/S0140-6736(20)30323-8
10.1007/s00259-020-04735-9
10.1093/cid/ciu053
10.1016/j.ejrad.2020.108991 |
Optical techniques, computed tomography and deep learning role in the diagnosis of COVID-19 pandemic towards increasing the survival rate of vulnerable populations. | • Severe lung complications can be explored using computed tomography during COVID-19 pandemic. • Ultra-low dose CT can enhance COVID-19 infected patients diagnostic capability. • Optically monitored CT along with deep learning is the best solution for diagnosis of COVID-19 during pandemic. • CT scans sensitivity (88 %... | Photodiagnosis and photodynamic therapy | 2020-06-21T00:00:00 | [
"Shahzad AhmadQureshi",
"Aziz UlRehman"
] | 10.1016/j.pdpdt.2020.101880
10.1007/s00330-020-06801-0
10.1007/s00330-020-06731-x
10.1016/j.pdpdt.2020.101836
10.1016/j.pdpdt.2020.101823
10.1016/j.ijantimicag.2020.105954
10.1097/rli.0000000000000670
10.21037/qims.2018.06.05
10.1080/14737159.2020.1766968
10.1007/s10489-020-01714-3
10.1101/2020.02.14.20023028
10.1109/t... |
Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet. | Presently, COVID-19 has posed a serious threat to researchers, scientists, health professionals, and administrations around the globe from its detection to its treatment. The whole world is witnessing a lockdown like situation because of COVID-19 pandemic. Persistent efforts are being made by the researchers to obtain ... | Chaos, solitons, and fractals | 2020-06-17T00:00:00 | [
"HarshPanwar",
"P KGupta",
"Mohammad KhubebSiddiqui",
"RubenMorales-Menendez",
"VaishnaviSingh"
] | 10.1016/j.chaos.2020.109944
10.22207/JPAM.14.SPL1.40
10.21203/rs.3.rs-26500/v1
10.1016/j.chaos.2020.109864
10.1007/s00521-018-3381-9
10.1186/s40708-020-00105-1 |
CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images. | The novel Coronavirus also called COVID-19 originated in Wuhan, China in December 2019 and has now spread across the world. It has so far infected around 1.8 million people and claimed approximately 114,698 lives overall. As the number of cases are rapidly increasing, most of the countries are facing shortage of testin... | Computer methods and programs in biomedicine | 2020-06-14T00:00:00 | [
"Asif IqbalKhan",
"Junaid LatiefShah",
"Mohammad MudasirBhat"
] | 10.1016/j.cmpb.2020.105581
10.1136/bmj.m641
10.1148/radiol.2020200432
10.1148/radiol.2020200343 |
Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. | In this study, a dataset of X-ray images from patients with common bacterial pneumonia, confirmed Covid-19 disease, and normal incidents, was utilized for the automatic detection of the Coronavirus disease. The aim of the study is to evaluate the performance of state-of-the-art convolutional neural network architecture... | Physical and engineering sciences in medicine | 2020-06-12T00:00:00 | [
"Ioannis DApostolopoulos",
"Tzani AMpesiana"
] | 10.1007/s13246-020-00865-4
10.1016/S2213-2600(20)30076-X
10.1109/TMI.2016.2553401
10.1561/2000000039
10.1016/j.cell.2018.02.010
10.1186/s40537-016-0043-6
10.1021/ci0342472 |
Dynamic evolution of COVID-19 on chest computed tomography: experience from Jiangsu Province of China. | To determine the patterns of chest computed tomography (CT) evolution according to disease severity in a large coronavirus disease 2019 (COVID-19) cohort in Jiangsu Province, China.
This retrospective cohort study was conducted from January 10, 2020, to February 18, 2020. All patients diagnosed with COVID-19 in Jiangsu... | European radiology | 2020-06-12T00:00:00 | [
"Yuan-ChengWang",
"HuanyuanLuo",
"SongqiaoLiu",
"ShanHuang",
"ZhenZhou",
"QianYu",
"ShijunZhang",
"ZhenZhao",
"YizhouYu",
"YiYang",
"DuolaoWang",
"ShenghongJu"
] | 10.1007/s00330-020-06976-6
10.1016/S1473-3099(20)30086-4
10.1016/S0140-6736(20)30183-5
10.1016/S0140-6736(10)61459-6
10.1148/rg.242035193
10.1002/jmv.25709 |
A Deep Neural Network to Distinguish COVID-19 from other Chest Diseases Using X-ray Images. | Scanning a patient's lungs to detect Coronavirus 2019 (COVID-19) may lead to similar imaging of other chest diseases. Thus, a multidisciplinary approach is strongly required to confirm the diagnosis. There are only a few works targeted at pathological x-ray images. Most of the works only target single disease detection... | Current medical imaging | 2020-06-05T00:00:00 | [
"SalehAlbahli"
] | 10.2174/1573405616666200604163954 |
Artificial Intelligence: Promise, Pitfalls, and Perspective. | null | JAMA | 2020-06-04T00:00:00 | [
"Angel NDesai"
] | 10.1001/jama.2020.8737 |
COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios. | The COVID-19 can cause severe pneumonia and is estimated to have a high impact on the healthcare system. Early diagnosis is crucial for correct treatment in order to possibly reduce the stress in the healthcare system. The standard image diagnosis tests for pneumonia are chest X-ray (CXR) and computed tomography (CT) s... | Computer methods and programs in biomedicine | 2020-05-24T00:00:00 | [
"Rodolfo MPereira",
"DiegoBertolini",
"Lucas OTeixeira",
"Carlos NSilla",
"Yandre M GCosta"
] | 10.1016/j.cmpb.2020.105532
10.1007/s11263-009-0315-0
10.1016/j.patcog.2013.11.029
10.1016/j.eswa.2018.01.038 |
A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis. | Coronavirus disease 2019 (COVID-19) has spread globally, and medical resources become insufficient in many regions. Fast diagnosis of COVID-19 and finding high-risk patients with worse prognosis for early prevention and medical resource optimisation is important. Here, we proposed a fully automatic deep learning system... | The European respiratory journal | 2020-05-24T00:00:00 | [
"ShuoWang",
"YunfeiZha",
"WeiminLi",
"QingxiaWu",
"XiaohuLi",
"MengNiu",
"MeiyunWang",
"XiaomingQiu",
"HongjunLi",
"HeYu",
"WeiGong",
"YanBai",
"LiLi",
"YongbeiZhu",
"LiusuWang",
"JieTian"
] | 10.1183/13993003.00775-2020
10.1016/S2213-2600(20)30079-5
10.1016/S2214-109X(20)30068-1
10.1183/13993003.00334-2020
10.1016/S1473-3099(20)30134-1
10.1183/13993003.00986-2018
10.1016/S2213-2600(18)30286-8
10.1016/S2213-2600(20)30003-5
10.1183/13993003.01216-2019
10.1016/j.media.2017.06.014
10.1101/2020.02.14.20023028
10... |
Generalizability of Deep Learning Tuberculosis Classifier to COVID-19 Chest Radiographs: New Tricks for an Old Algorithm? | null | Journal of thoracic imaging | 2020-05-20T00:00:00 | [
"Paul HYi",
"Tae KyungKim",
"Cheng TingLin"
] | 10.1097/RTI.0000000000000532 |
Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography. | Many COVID-19 patients infected by SARS-CoV-2 virus develop pneumonia (called novel coronavirus pneumonia, NCP) and rapidly progress to respiratory failure. However, rapid diagnosis and identification of high-risk patients for early intervention are challenging. Using a large computed tomography (CT) database from 3,77... | Cell | 2020-05-18T00:00:00 | [
"KangZhang",
"XiaohongLiu",
"JunShen",
"ZhihuanLi",
"YeSang",
"XingwangWu",
"YunfeiZha",
"WenhuaLiang",
"ChengdiWang",
"KeWang",
"LinsenYe",
"MingGao",
"ZhongguoZhou",
"LiangLi",
"JinWang",
"ZehongYang",
"HuiminCai",
"JieXu",
"LeiYang",
"WenjiaCai",
"WenqinXu",
"ShaoxuWu",
... | 10.1016/j.cell.2020.04.045
10.1109/ICPR.2018.8546325
10.1016/s2213-2600(20)30079-5 |
Extracting Possibly Representative COVID-19 Biomarkers from X-ray Images with Deep Learning Approach and Image Data Related to Pulmonary Diseases. | While the spread of COVID-19 is increased, new, automatic, and reliable methods for accurate detection are essential to reduce the exposure of the medical experts to the outbreak. X-ray imaging, although limited to specific visualizations, may be helpful for the diagnosis. In this study, the problem of automatic classi... | Journal of medical and biological engineering | 2020-05-16T00:00:00 | [
"Ioannis DApostolopoulos",
"Sokratis IAznaouridis",
"Mpesiana ATzani"
] | 10.1007/s40846-020-00529-4
10.1007/s13246-020-00865
10.1109/TMI.2016.2553401
10.1631/FITEE.1700808
10.1016/j.cell.2018.02.010
10.1109/TMI.2016.2528162
10.1109/TMI.2018.2791721
10.3389/fnins.2018.00804
10.1164/rccm.201802-0350LE
10.1097/COH.0b013e32833ed177
10.1002/mp.12820
10.2214/AJR.16.17224
10.1111/tmi.13383 |
Deep learning-based multi-view fusion model for screening 2019 novel coronavirus pneumonia: A multicentre study. | To develop a deep learning-based method to assist radiologists to fast and accurately identify patients with COVID-19 by CT images.
We retrospectively collected chest CT images of 495 patients from three hospitals in China. 495 datasets were randomly divided into 395 cases (80%, 294 of COVID-19, 101 of other pneumonia)... | European journal of radiology | 2020-05-15T00:00:00 | [
"XiangjunWu",
"HuiHui",
"MengNiu",
"LiangLi",
"LiWang",
"BingxiHe",
"XinYang",
"LiLi",
"HongjunLi",
"JieTian",
"YunfeiZha"
] | 10.1016/j.ejrad.2020.109041
10.1016/S0140-6736(20)30154-9
10.1016/j.ejrad.2020.108961
10.1016/S1473-3099(20)30086-4
10.1148/radiol.2020200642
10.1109/TMI.2018.2876510
10.1001/jamanetworkopen.2019.2561
10.1109/TMI.2017.2759102
10.1038/s41591-018-0177-5
10.1183/13993003.00986-2018
10.1016/j.tranon.2017.08.007
10.1109/Cvp... |
Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound. | Deep learning (DL) has proved successful in medical imaging and, in the wake of the recent COVID-19 pandemic, some works have started to investigate DL-based solutions for the assisted diagnosis of lung diseases. While existing works focus on CT scans, this paper studies the application of DL techniques for the analysi... | IEEE transactions on medical imaging | 2020-05-15T00:00:00 | [
"SubhankarRoy",
"WilliMenapace",
"SebastiaanOei",
"BenLuijten",
"EnricoFini",
"CristianoSaltori",
"IrisHuijben",
"NishithChennakeshava",
"FedericoMento",
"AlessandroSentelli",
"EmanuelePeschiera",
"RiccardoTrevisan",
"GiovanniMaschietto",
"ElenaTorri",
"RiccardoInchingolo",
"AndreaSmar... | 10.1109/TMI.2020.2994459 |
Using X-ray images and deep learning for automated detection of coronavirus disease. | Coronavirus is still the leading cause of death worldwide. There are a set number of COVID-19 test units accessible in emergency clinics because of the expanding cases daily. Therefore, it is important to implement an automatic detection and classification system as a speedy elective finding choice to forestall COVID-1... | Journal of biomolecular structure & dynamics | 2020-05-14T00:00:00 | [
"KhalidEl Asnaoui",
"YounessChawki"
] | 10.1080/07391102.2020.1767212
10.1080/07391102.2020.1758790
10.1080/07391102.2020.1763199
10.1016/j.cmpb.2018.01.017
10.1080/07391102.2020.1754293
10.1016/j.patrec.2019.11.013
10.1184/R1/6606860.v1
10.1080/07391102.2020.1758788
10.1016/j.pdisas.2020.100091
10.1016/j.onehlt.2020.100124
10.1080/07391102.2020.1761882
10.1... |
Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets. | Under the global pandemic of COVID-19, the use of artificial intelligence to analyze chest X-ray (CXR) image for COVID-19 diagnosis and patient triage is becoming important. Unfortunately, due to the emergent nature of the COVID-19 pandemic, a systematic collection of CXR data set for deep neural network training is di... | IEEE transactions on medical imaging | 2020-05-13T00:00:00 | [
"YujinOh",
"SangjoonPark",
"Jong ChulYe"
] | 10.1109/TMI.2020.2993291 |
Multicenter cohort study demonstrates more consolidation in upper lungs on initial CT increases the risk of adverse clinical outcome in COVID-19 patients. | Theranostics | 2020-05-07T00:00:00 | [
"QianYu",
"YuanchengWang",
"ShanHuang",
"SongqiaoLiu",
"ZhenZhou",
"ShijunZhang",
"ZhenZhao",
"YizhouYu",
"YiYang",
"ShenghongJu"
] | 10.7150/thno.46465 | |
COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images. | The Coronavirus Disease 2019 (COVID-19) outbreak has a tremendous impact on global health and the daily life of people still living in more than two hundred countries. The crucial action to gain the force in the fight of COVID-19 is to have powerful monitoring of the site forming infected patients. Most of the initial ... | Medical hypotheses | 2020-04-29T00:00:00 | [
"FerhatUcar",
"DenizKorkmaz"
] | 10.1016/j.mehy.2020.109761
10.1016/j.ijantimicag.2020.105924
10.1001/jama.2020.1585
10.1016/j.cca.2020.03.009
10.1093/clinchem/hvaa029
10.1016/j.cell.2018.02.010
10.1148/radiol.2017162326
10.1016/j.mehy.2019.109426
10.1016/j.mehy.2019.109433
10.1109/ACCESS.2020.2982017
10.11989/JEST.1674-862X.80904120
10.1016/j.patcog.... |
Classification of COVID-19 patients from chest CT images using multi-objective differential evolution-based convolutional neural networks. | Early classification of 2019 novel coronavirus disease (COVID-19) is essential for disease cure and control. Compared with reverse-transcription polymerase chain reaction (RT-PCR), chest computed tomography (CT) imaging may be a significantly more trustworthy, useful, and rapid technique to classify and evaluate COVID-... | European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology | 2020-04-28T00:00:00 | [
"DilbagSingh",
"VijayKumar",
"NoneVaishali",
"ManjitKaur"
] | 10.1007/s10096-020-03901-z |
Coronavirus Disease 2019 Deep Learning Models: Methodologic Considerations. | null | Radiology | 2020-04-04T00:00:00 | [
"Andrew M VDadário",
"Joselisa P Qde Paiva",
"Rodrigo CChate",
"Birajara SMachado",
"GilbertoSzarf"
] | 10.1148/radiol.2020201178
10.1148/radiol.2020200905 |
Deep Learning Localization of Pneumonia: 2019 Coronavirus (COVID-19) Outbreak. | null | Journal of thoracic imaging | 2020-03-25T00:00:00 | [
"BrianHurt",
"SethKligerman",
"AlbertHsiao"
] | 10.1097/RTI.0000000000000512 |
Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy. | Background Coronavirus disease 2019 (COVID-19) has widely spread all over the world since the beginning of 2020. It is desirable to develop automatic and accurate detection of COVID-19 using chest CT. Purpose To develop a fully automatic framework to detect COVID-19 using chest CT and evaluate its performance. Material... | Radiology | 2020-03-20T00:00:00 | [
"LinLi",
"LixinQin",
"ZeguoXu",
"YoubingYin",
"XinWang",
"BinKong",
"JunjieBai",
"YiLu",
"ZhenghanFang",
"QiSong",
"KunlinCao",
"DaliangLiu",
"GuishengWang",
"QizhongXu",
"XishengFang",
"ShiqinZhang",
"JuanXia",
"JunXia"
] | 10.1148/radiol.2020200905
10.1148/radiol.2020200642
10.1148/radiol.2020200432 |
False-Negative Results of Real-Time Reverse-Transcriptase Polymerase Chain Reaction for Severe Acute Respiratory Syndrome Coronavirus 2: Role of Deep-Learning-Based CT Diagnosis and Insights from Two Cases. | The epidemic of 2019 novel coronavirus, later named as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is still gradually spreading worldwide. The nucleic acid test or genetic sequencing serves as the gold standard method for confirmation of infection, yet several recent studies have reported false-negati... | Korean journal of radiology | 2020-03-17T00:00:00 | [
"DashengLi",
"DaweiWang",
"JianpingDong",
"NanaWang",
"HeHuang",
"HaiwangXu",
"ChenXia"
] | 10.3348/kjr.2020.0146
10.1101/2020.02.07.937862
10.1148/radiol.2020200343
10.1148/radiol.2020200230 |
Optimizing MRF-ASL scan design for precise quantification of brain hemodynamics using neural network regression. | Arterial Spin Labeling (ASL) is a quantitative, non-invasive alternative for perfusion imaging that does not use contrast agents. The magnetic resonance fingerprinting (MRF) framework can be adapted to ASL to estimate multiple physiological parameters simultaneously. In this work, we introduce an optimization scheme to... | Magnetic resonance in medicine | 2019-11-22T00:00:00 | [
"AnishLahiri",
"Jeffrey AFessler",
"LuisHernandez-Garcia"
] | 10.1002/mrm.28051 |
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