PyTorch has various advantages when it comes to MIL modeling. It offers an intuitive way to create dynamic computational graphs with flexible control flow which is great for rapid research experimentation. The map-style datasets, configurable sampler and batch-samplers allow us to customize how we construct bags of patches, enabling faster experimentation. Since MIL models are IO heavy, data parallelism and pythonic data loaders make the task very efficient and user friendly. Lastly, the object-oriented nature of PyTorch enables building of reusable modules which aid in the rapid experimentation, maintainable implementation and ease of building compositional components of the pipeline.
Exploring spatial tissue organization with GNNs in PyTorch


