Deep learning with PyTorch | Medical Imaging Competitions

Learn how to solve different deep learning problems using Pytorch and participate in medical imaging competitions

What you'll learn
- Learn how to use PyTorch Lightning
- Participate and win medical imaging competetions
- Get hands on experience with practical deep learning in medical imaging
- Learn Classification, Regression and Segmentation
- Submit submission files in competetions
- Learn ensemble learning to win competitions

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
This course is outdated because it is based on pytorch lightning and alot of thing has been changed since the release of this course.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 coursePyTorch lightningis 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

Modify binary cross-entropy loss to focal loss

Use custom metric provided by competition organizer to get the evaluation

Get predictions of test set

Capstone Project (Covid-19 Infection Percentage Estimation)

How to come up with a solution

Code walk-through

The secret sauce of model ensemble

Semantic Segmentation

Data download and read data from nii.gz

Apply augmentation to image and mask simultaneously

Train model on NIfTI images

Plot test images and corresponding ground truth and predicted masks

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
More Info