Deep learning with PyTorch Medical Imaging Competitions

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Price: 24.99$

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 Res Net, Dense Net 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 Py Torch lightning is used The course covers the following topics Binary Classification Get the data Read data Apply augmentation How data flows from folders to GPUTrain 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 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 Capstone Project (Covid-19 Infection Percentage Estimation)How to come up with a solution Code walk-through The secret sauce of model ensemble Semantic Segmentation Data download and read data from nii. gz Apply augmentation to image and mask simultaneously Train model on NIf TI images Plot test images and corresponding ground truth and predicted masks