Deep learning in Action | Medical Imaging Competitions |2022

What you'll learn
Learn how to use PyTorch Lightning
Participate and win medical imaging based deep learning competetions
Get hands on experience with practical deep learning in medical imaging
Get experience with different augmentations techniques
Submit submission files in competetions
Learn ensemble learning to win competetions

Requirements
Should have good understanding of python
Have basic theoratical knowledge of deep learning (CNNs, optimizers, loss function etc)
Have done atleast one project in machine learning or deep learning in any framework
Description
Greetings. This course is not intended for beginners and it is more piratically oriented. Though I tried my best to explain why I performed a particular step but as said I put little to no effort on explaining what is Convolution neural networks, how optimizer works, how ResNet, DenseNet model was created etc.

My focus was mainly on how to participate in a competition, how to get data and train a model on that data and how to make a submission.

The course cover 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 5 fold cross validations when images are in 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 image simultaneously

Make a parallel network to take two images simultaneously

Modify binary cross entropy loss to focal loss

Use custom metric provided by competition organizer to get evaluation

Get predictions of test set

Capstone Project (Covid-19 Infection Percentage Estimation)

How to come up with a solution

Code walk through

Secret sauce of model ensemble

Who this course is for
For itermediate users who know about python and machine learning
Have done cats and dogs classification problem but not sure how to handle a large data or problem
Want to step in medical imaging and build a portfolio
Want to win kaggle, codalab and grandchallenge comeptetions

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