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

Published on: 2023-02-07 14:57:00

Categories: 28

Descriptions

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