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Deep-Learning-with-PyTorch.pdf |
INPUT
REPRESENTATION(VALUES OF PIXELS)158 186 2200.19
0.230.460.77...0.91 0.010.0
0.520.910.0...0.74
0.45...172 175 ...
INTERMEDIATE
REPRESENTATIONS
SIMILAR INPUTS
SHOULD LEAD TOCLOSE REPRESENTATIONS(ESPECIALlY AT DEePER LEVELS)OUTPUT
REPRESENTATION(PROBABILITY OF CLASsES)“SUN”
“SEASIdE”“SCENERY” |
Deep-Learning-with-PyTorch.pdf | Stevens ● Antiga ● Viehmann
ISBN: 978-1-61729-526-3
Although many deep learning tools use Python, the
PyTorch library is truly Pythonic. Instantly familiar to anyone who knows PyData tools like NumPy and
scikit-learn, PyTorch simplifi es deep learning without sacrifi c-
ing advanced features. It’s excellent for build... |
Deep Learning in Medical Image Analysis.pdf | Deep Learning in
Medical Image
Analysis
Dr. Hichem Felouat
hichemfel@gmail.com
https://www.researchgate.net/profile/Hichem_Felouat
https://www.linkedin.com/in/hichemfelouat
|
Deep Learning in Medical Image Analysis.pdf | 2 Hichem Felouat - hichemfel@gmail.com - AlgeriaMedical Images
•Medical imaging is the technique and process of creating
visual representations of the interior of a body for clinical
analysis and medical intervention, as well as visual
representation of the function of some organs or tissues.
•Medical imaging seeks ... |
Deep Learning in Medical Image Analysis.pdf | 3 Hichem Felouat - hichemfel@gmail.com - AlgeriaMedical Image Modalities
•X-ray radiography - US: Ultrasound - MR/MRI/DMRI: Magnetic Resonance Imaging - PET: Positron Emission
Tomography - MG: Mammography - CT: Computed Tomography - RGB: Optical Images.
|
Deep Learning in Medical Image Analysis.pdf | 4 Hichem Felouat - hichemfel@gmail.com - AlgeriaMedical Image Visualization
•Visualization is the process of exploring, transforming, and viewing
data as images to gain understanding and insight into the data, which
requires fast interactive speed and high image quality.
Anatomist :
https://brainvisa.info/web/ |
Deep Learning in Medical Image Analysis.pdf | 5 Hichem Felouat - hichemfel@gmail.com - Algeria
Plotly [1]
1)https://plotly.com/python/visualizing-mri-volume-slices/
2)https://nilearn.github.io/stable/index.html
3)https://nipy.org/nibabel/coordinate_systems.html
Medical Image Visualization
Nilearn [2]
NiBabel [3] |
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 6Medical Image Data I/O
https://nipy.org/nibabel/coordinate_systems.html |
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 7Deep Learning DL
DL is a subfield of ML, developed by several researchers.
|
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 8Deep Learning DL
|
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 9Deep Learning DL
|
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 10Deep Learning DL
Several DL models have been proposed :
•Convolutional neural networks (CNNs)
•Autoencoders (Aes)
•Recurrent neural networks (RNNs)
•Generative adversarial networks (GANs)
• Faster RCNN and Mask RCNN
•U-Net
•Vision Transformer (ViT)
•Graph Neural Networks... |
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 11Deep Learning DL
|
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 12Deep Learning DL
•In DL area, there are many different tasks: Image Classification, Regression, Object
Localization, Object Detection, Instance Segmentation, Image captioning, etc..
|
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 13Image Classification for Medical Image Analysis
•Convolutional neural network (CNN) is the dominant classification
framework for image analysis. |
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 14Image Classification for Medical Image Analysis - The eyes
of CNN
•CNN is designed for working with two-dimensional image data, also they can be used
with one-dimensional and three-dimensional data. |
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 15A simple 2D CNN
|
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 16A simple 3D CNN
|
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 17Image Regression for Medical Image Analysis
Brain age prediction using deep learning :
https://www.nature.com/articles/s41467-019-13163-9 |
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 18Medical Image Captioning Using DL
Medical Image Captioning Using Optimized Deep Learning Model :
https://www.hindawi.com/journals/cin/2022/9638438/ |
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 19Transfer Learning TL
•Transfer learning is a machine learning method where a model
developed for a task is reused as the starting point for a model on a
second task.
•The intuition behind transfer learning for image classification is that if a
model is trained on a la... |
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 20Transfer Learning TL
•It is generally not a good idea to train a very large DNN from scratch: instead, you
should always try to find an existing neural network that accomplishes a similar task
to the one you are trying to tackle then reuse the lower layers of this netw... |
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 21Transfer Learning TL
|
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 22Available models in Keras:
Models for image classification with weights trained on ImageNet:
https://keras.io/applications/
Transfer Learning TL |
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 23Transfer Learning TL
base_model = keras.applications.xception.Xception(weights="imagenet", include_top=False)
avg = keras.layers.GlobalAveragePooling2D()(base_model.output)
output = keras.layers.Dense(n_classes, activation="softmax")(avg)
model = keras.Model(inputs=base_... |
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 24Object Detection
•Localizing an object in a picture means predicting a bounding
box around the object and can be expressed as a regression task.
|
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 25Object Detection
Problem: the dataset does not have bounding boxes
around the objects, how can we train our model?
•We need to add them ourselves. This is often one of the
hardest and most costly parts of a Machine Learning project:
getting the labels.
•It is a good ... |
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 26Object Detection
•An image labeling or annotation tool is used to label the images for
bounding box object detection and segmentation.
Open-source image labeling tool like :
•VGG Image
•Annotator
•LabelImg
•OpenLabeler
•ImgLab
Commercial tool like :
•LabelBox
•Supervi... |
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 27Object Detection - VGG
VGG Image: http://www.robots.ox.ac.uk/~vgg/software/via/ |
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 28Object Detection - labelImg
|
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 29Object Detection
•The MSE often works fairly well as a cost function to train the model,
but it is not a great metric to evaluate how well the model can predict
bounding boxes.
•The most common metric for this is the Intersection over Union (IoU).
|
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 30Object Detection
mean Average Precision :
In order to calculate mAP, we draw a series of precision-recall curves with the IoU
threshold set at varying levels of difficulty. In COCO evaluation, the IoU threshold
ranges from 0.5 to 0.95 with a step size of 0.05 represen... |
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 31Object Detection
(C, X,Y, W, H)raw image
must have the same
sizeimage labeling
(C, X,Y, W, H)model
•Each item should be a tuple of the form :
(images,
(class_labels, bounding_boxes) )
|
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 32Object Detection
In general, object detectors have three (3) main components:
1)The backbone that extracts features from the given image.
2)The feature network that takes multiple levels of features from the
backbone as input and outputs a list of fused features that ... |
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 33Object Detection - Faster RCNN
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://arxiv.org/abs/1506.01497Feature Network
Region Proposal Network
Backbone Class/Box
Network |
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 34Instance Segmentation - labelme
•Instance Segmentation aims to predicting the object class-label and the pixel-specific
object instance-mask. |
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 35Instance Segmentation - Mask R-CNN
Backbone Feature Network
Region Proposal Network
Class/Box/Mask
Network |
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 36Instance Segmentation - Mask R-CNN
https://github.com/hichemfelouat/my-codes-of-machine-learning/blob/master/Mask_RCNN_TF2OD_Custom_dataset.ipynb |
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 37YOLO
•You Only Look Once (YOLO) is an algorithm that uses
convolutional neural networks for object detection.
• It is one of the faster object detection algorithms out there.
•It is a very good choice when we need real-time detection,
without loss of too much accurac... |
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 38•Detectron2 was built by Facebook AI Research (FAIR) to
support rapid implementation and evaluation of novel computer
vision research.
•Detectron2 is now implemented in PyTorch.
•Detectron2 is flexible and extensible, and able to provide fast
training on single or mu... |
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 39Detectron2
Detectron2 Model Zoo : https://github.com/facebookresearch/detectron2/blob/master/MODEL_ZOO.md |
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 40TensorFlow 2 Object Detection API
•The TensorFlow Object Detection API is an open-source
framework built on top of TensorFlow that makes it easy to
construct, train, and deploy object detection models.
• The TensorFlow Object Detection API allows you to train a
collec... |
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 41TensorFlow 2 Object Detection API
Model Zoo : https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md |
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 423D-Unet
|
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 433D-Unet
|
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 44Autoencoders in Medical Imaging
Architecture of the denoising autoencoder model, with an example low-SNR, single-repetition dM raw image (left), and the corresponding high-SNR
dM mean image.
Combined Denoising and Suppression of Transient Artifacts in Arterial Spin Labe... |
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 45Autoencoders in Medical Imaging
|
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 46Generative Adversarial Networks GANs in Medical
Imaging
GANs for medical image analysis :
https://doi.org/10.1016/j.artmed.2020.101938 |
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 47Transformers in Medical Imaging
Vision Transformer (ViT) for Image Classification (cifar10 dataset) :
https://github.com/hichemfelouat/my-codes-of-machine-learning/blob/master/Vision_Transformer_(ViT)_for_Image_Classification_(cifar10_dataset).ipynb |
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 48Transformers in Medical Imaging
Transformers in Medical Imaging: A Survey
https://arxiv.org/abs/2201.09873v1 |
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 49Transformers in Medical Imaging
|
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 50Graph Neural Network in Medical Image Analysis
BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis :
https://doi.org/10.1016/j.media.2021.102233
Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future :
https://arxiv.org/ab... |
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 51Self-Supervised and Semi-Supervised Learning
In the self-supervised learning technique, the model depends on the underlying structure
of data to predict outcomes. It involves no labelled data. However, in semi-supervised
learning, we still provide a small amount of lab... |
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 52Self-Supervised and Semi-Supervised Learning
Uncertainty Guided Semi-supervised Segmentation of Retinal Layers in OCT Images
https://link.springer.com/chapter/10.1007/978-3-030-32239-7_32
Semi-Supervised Learning in Computer Vision
https://amitness.com/2020/07/semi-super... |
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 53Open Set Learning OSL
Traditional supervised learning aims to train a classifier in the closed-set world, where training and test
samples share the same label space. Open set learning (OSL) is a more challenging and realistic setting,
where there exist test samples fro... |
Deep Learning in Medical Image Analysis.pdf | Hichem Felouat - hichemfel@gmail.com - Algeria 54Thanks For Your
Attention
Hichem Felouat ... |
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