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:
- Making training/testing databases,
- Training a model,
- Visualizing results in the validation set,
- Generating output.