Deep learning in Action – Medical Imaging Competitions 2022


Deep learning in Action – Medical Imaging Competitions 2022
Genre: eLearning | MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.68 GB | Duration: 3h 23m
Learn how to solve different deep learning problems and participate in different medical imaging competitions


What you’ll learn
Learn how to use PyTorch Lightning
Participate and win medical imaging based deep learning competetions
Get hands on experience with practical deep learning in medical imaging
Get experience with different augmentations techniques
Submit submission files in competetions
Learn ensemble learning to win competetions
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
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 on explaining what is Convolution neural networks, how 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 cover 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 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 image 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 evaluation
Get predictions of test set
Capstone Project (Covid-19 Infection Percentage Estimation)
How to come up with a solution
Code walk through
Secret sauce of model ensemble
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
https://www.udemy.com/course/deep-learning-in-action-medical-imaging-competitions/

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