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Since these models are trained end-to-end, they offer a powerful way to go directly from a gigapixel whole slide image to a single label. Due to their wide applicability to different biological problems, two aspects of their implementation and deployment are important:

Configurable control over each part of the pipeline including the data loaders, the modular parts of the model, and their interaction with each other.
Ability to rapidly iterate through the ideate-implement-experiment-productionize loop.
