mlsanityData to 14 Oct 2026

Evaluating histopathology transfer learning with ChampKit

arXiv 2206.06862 ↗First posted 14 Jun 2022q-bio.QM cs.CV cs.LGcited by 7

Not on the map. Why?

Abstract (arXiv)

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 .

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 35 of its 136 references that are in the map. First posted on arXiv 14 Jun 2022.

Builds on

35 of 136 references.

References · 35

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23 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 2; 2025: 1; 2026: 1.

7 papers.

  1. 2023A General-Purpose Self-Supervised Model for Computational Pathologycited by 42
  2. 2024RudolfV: A Foundation Model by Pathologists for Pathologistscited by 41
  3. 2025Atlas: A Novel Pathology Foundation Model by Mayo Clinic, Charit'e, and Aignosticscited by 11
  4. 2024A New Era in Computational Pathology: A Survey on Foundation and Vision-Language Modelscited by 9
  5. 2023Fine-Grained Self-Supervised Learning with Jigsaw Puzzles for Medical Image Classificationcited by 7
  6. 2026Atlas 2 -- Foundation models for clinical deploymentcited by 7
  7. 2023ProtoDiv: Prototype-guided Division of Consistent Pseudo-bags for Whole-slide Image Classificationcited by 5