  Histopathology remains the gold standard for diagnosis of various cancers.
Recent advances in computer vision, specifically deep learning, have
facilitated the analysis of histopathology images for various tasks, including
immune cell detection and microsatellite instability classification. The
state-of-the-art for each task often employs base architectures that have been
pretrained for image classification on ImageNet. The standard approach to
develop classifiers in histopathology tends to focus narrowly on optimizing
models for a single task, not considering the aspects of modeling innovations
that improve generalization across tasks. Here we present ChampKit
(Comprehensive Histopathology Assessment of Model Predictions toolKit): an
extensible, fully reproducible benchmarking toolkit that consists of a broad
collection of patch-level image classification tasks across different cancers.
ChampKit enables a way to systematically document the performance impact of
proposed improvements in models and methodology. ChampKit source code and data
are freely accessible at https://github.com/kaczmarj/champkit .
