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    Deep learning in Action | Medical Imaging Competitions |2022

    Posted By: BlackDove
    Deep learning in Action | Medical Imaging Competitions |2022

    Deep learning in Action | Medical Imaging Competitions |2022
    Genre: eLearning | MP4 | Video: h264, 1280x720 | 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