Deep learning with PyTorch | Medical Imaging Competitions

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

Binary Classification

Get the data

Read data

Apply augmentation

How data flows from folders to GPU

Train a model

Get accuracy metric and loss

Multi–class classification (CXR–covid19 competition)

Albumentations augmentations

Write a custom data loader

Use publicly pre–trained model on XRay

Use learning rate scheduler

Use different callback functions

Do five fold cross–validations when images are in a folder

Train, save and load model

Get test predictions via ensemble learning

Submit predictions to the competition page

Multi–label classification (ODIR competition)

Apply augmentation on two images simultaneously

Make a parallel network to take two images simultaneously