Digital pathology classification using Pytorch + Densenet

In this blog post, we discuss how to train a DenseNet style deep learning classifier, using Pytorch, for differentiating between different types of lymphoma cancer. This post and code are based on the post discussing segmentation using U-Net and is thus broken down into the same 4 components:

  1. Making training/testing databases,
  2. Training a model,
  3. Visualizing results in the validation set,
  4. Generating output.

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