deep-learning-with-pytorch-medical-imaging-competitions

\ 0:00 Introduction Classification\ 7:01 Read, split and display images via PyTorch 24:50 Understand the flow of data to model, loss function and metrics 32:10 Write a structure for Pytorch Lightning Module class 41:52 Complete structure and train the model class classification\ 1:01:31 Custom dataset class for albumentation 1:16:15 XRay pretrained model in Pytorch Lightning 1:25:59 Train and validate the model 1:34:41 Use sampler and sheduler 1:46:54 Five fold cross validation 1:55:27 Get predictions for the test set 2:10:39 Submit file to the competetion Classification\ 2:12:00 Understand the data 2:16:42 Augmentation two images simultaneoulsy 2:24:35 Read two images simultaneoulsy 2:35:29 Create dual input model via TIMM 2:43:51 Create Lightning module and and convert BCELoss to focal loss 2:51:17 Train and validate the model 2:57:36 Create file for test set Project\ 3:04:04 Undertand the competetion 3:08:35 Code a Wining Solution \ 3:23:47 Read and plot Xray Images ( files) 3:40:26 Apply augmentation 3:51:35 Train model using pytorch lightning 4:04:22 Plot predicted masks