Udemy – Deep learning with PyTorch | Medical Imaging Competitions 2021-12

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)

• lbumentations 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

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

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

Specificatoin of Deep learning with PyTorch | Medical Imaging Competitions

• Publisher : Udemy
• Teacher : Talha Anwar
• Language : English
• Level : Intermediate
• Number of Course : 21
• Duration : 3 hours and 3 minutes

Content of Deep learning with PyTorch | Medical Imaging Competitions

# Introduction
# Section I Binary Classification
# Section 3 Multi class classification
# Mutilabel Classification
# Capstone Project

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

Pictures

Sample Clip

Installation Guide

Extract the files and watch with your favorite player

Subtitle : English

Quality: 720p

Download Links

Download Part 1 – 1 GB

Download Part 2 – 595 MB

File size

1.58 GB