Deep learning with PyTorch | Medical Imaging Competitions

Deep learning with PyTorch | Medical Imaging Competitions, available at $59.99, has an average rating of 3.95, with 35 lectures, 2 quizzes, based on 14 reviews, and has 300 subscribers.

You will learn about 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 This course is ideal for individuals who are For itermediate users who know about python and machine learning or Have done cats and dogs classification problem but not sure how to handle a large data or problem or Want to step in medical imaging and build a portfolio or Want to win kaggle, codalab and grandchallenge comeptetions It is particularly useful for For itermediate users who know about python and machine learning or Have done cats and dogs classification problem but not sure how to handle a large data or problem or Want to step in medical imaging and build a portfolio or Want to win kaggle, codalab and grandchallenge comeptetions.

Enroll now: Deep learning with PyTorch | Medical Imaging Competitions

Summary

Title: Deep learning with PyTorch | Medical Imaging Competitions

Price: $59.99

Average Rating: 3.95

Number of Lectures: 35

Number of Quizzes: 2

Number of Published Lectures: 35

Number of Published Quizzes: 2

Number of Curriculum Items: 37

Number of Published Curriculum Objects: 37

Original Price: $22.99

Quality Status: approved

Status: Live

What You Will 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 Should Attend

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

Target Audiences

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

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 course PyTorch lightning is used

The course covers the following topics

Binary Classification
1. Get the data
2. Read data
3. Apply augmentation
4. How data flows from folders to GPU
5. Train a model
6. Get accuracy metric and loss
Multi-class classification (CXR-covid19 competition)
1. Albumentations augmentations
2. Write a custom data loader
3. Use publicly pre-trained model on XRay
4. Use learning rate scheduler
5. Use different callback functions
6. Do five fold cross-validations when images are in a folder
7. Train, save and load model
8. Get test predictions via ensemble learning
9. Submit predictions to the competition page
Multi-label classification (ODIR competition)
1. Apply augmentation on two images simultaneously
2. Make a parallel network to take two images simultaneously
3. Modify binary cross-entropy loss to focal loss
4. Use custom metric provided by competition organizer to get the evaluation
5. Get predictions of test set
Capstone Project (Covid-19 Infection Percentage Estimation)
1. How to come up with a solution
2. Code walk-through
3. The secret sauce of model ensemble
Semantic Segmentation
1. Data download and read data from nii.gz
2. Apply augmentation to image and mask simultaneously
3. Train model on NIfTI images
4. Plot test images and corresponding ground truth and predicted masks

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Resources

Chapter 2: Binary Classification

Lecture 1: Read, split and display images via PyTorch

Lecture 2: Understand the flow of data to model, loss function and metrics

Lecture 3: Write a structure for Pytorch Lightning Module class

Lecture 4: Complete structure and train the model

Chapter 3: Multi class classification

Lecture 1: Custom dataset class for albumentation

Lecture 2: XRay pretrained model in Pytorch Lightning

Lecture 3: Train and validate the model

Lecture 4: Use sampler and sheduler

Lecture 5: Five fold cross validation

Lecture 6: Get predictions for the test set

Lecture 7: Submit file to the competetion

Chapter 4: Mutilabel Classification

Lecture 1: Understand the data

Lecture 2: Augmentation two images simultaneoulsy

Lecture 3: Read two images simultaneoulsy

Lecture 4: Create dual input model via TIMM

Lecture 5: Create Lightning module and and convert BCELoss to focal loss

Lecture 6: Train and validate the model

Lecture 7: Create submissions.csv file for test set

Chapter 5: Capstone Project

Lecture 1: Undertand the competetion

Lecture 2: Code a Wining Solution

Lecture 3: Research article discussing 2nd position

Chapter 6: Segmentation

Lecture 1: Prerequisite for this section

Lecture 2: Read and plot Xray Images (nii.gz files)

Lecture 3: Apply augmentation

Lecture 4: Train model using pytorch lightning

Lecture 5: Why We Squeeze the ground truth channels

Lecture 6: Plot predicted masks

Chapter 7: 3D CNN Video Classification | Additional

Lecture 1: Seting up the project

Lecture 2: Write Dataloader and do augmentations

Lecture 3: Define 3D CNN Model

Lecture 4: Define training, validation and test steps

Lecture 5: Train the model

Lecture 6: Evaluate the model

Instructors

Talha Anwar
Biomedical Engineer | Data Scientist

Rating Distribution

1 stars: 2 votes
2 stars: 0 votes
3 stars: 1 votes
4 stars: 2 votes
5 stars: 9 votes

Frequently Asked Questions

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