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 into explaining what is Convolution neural networks, how the 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 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 5 fold cross validations when images are in the 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
Modify binary cross entropy loss to focal loss
Use custom metric provided by competition organizer to get an evaluation
Get predictions of the test set
Capstone Project (Covid 19 Infection Percentage Estimation)
How to come up with a solution
Code walkthrough
The secret sauce of model ensemble
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SKILLS YOU WILL GAIN
Participate and win medical imaging based deep learning competitions
Get experience with different augmentations techniques
Learn how to do segmentation with UNET
WHAT YOU WILL LEARN
Get hands on experience with practical deep learning in medical imaging
Submit submission files in competitions
Learn ensemble learning to win competitions