Deep learning with PyTorch | Medical Imaging Competitions

Description

This course is outdated because it is based on pytorch lightning and alot of thing has been changed since the release of this course. Further some of datasets in this course are no more available for public anymore. So I am not providing support for this course. I want to make this course free, but udemy is not allowing to do so because of content length. The reason why I am not archiving this course, because its still relevant if you want to gain concept of medical imaging competition.

Greetings. This course is not intended for beginners, and it is more practically oriented. Though I tried my best to explain why I performed a particular step, I put little to no effort into explaining basic concepts such as Convolution neural networks, how the optimizer works, how ResNet, DenseNet model was created etc. This course is for those who have worked on CIFAR, MNIST data and want to work in real-life scenarios

My focus was mainly on how to participate in a competition, get data and train a model on that data, and make a submission. In this course PyTorch lightning is used

The course covers the following topics

1. Binary Classification

   1. Get the data

   2. Read data

   3. Apply augmentation

   4. How data flows from folders to GPU

   5. Train a model

   6. Get accuracy metric and loss

2. Multi-class classification (CXR-covid19 competition)

   1. Albumentations augmentations

   2. Write a custom data loader

   3. Use publicly pre-trained model on XRay

   4. Use learning rate scheduler

   5. Use different callback functions

   6. Do five fold cross-validations when images are in a folder

   7. Train, save and load model

   8. Get test predictions via ensemble learning

   9. Submit predictions to the competition page

3. Multi-label classification (ODIR competition)

   1. Apply augmentation on two images simultaneously

   2. Make a parallel network to take two images simultaneously

   3. Modify binary cross-entropy loss to focal loss

   4. Use custom metric provided by competition organizer to get the evaluation

   5. Get predictions of test set

4. Capstone Project (Covid-19 Infection Percentage Estimation)

   1. How to come up with a solution

   2. Code walk-through

   3. The secret sauce of model ensemble

5. Semantic Segmentation

   1. Data download and read data from nii.gz

   2. Apply augmentation to image and mask simultaneously

   3. Train model on NIfTI images

   4. Plot test images and corresponding ground truth and predicted masks
      

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