mlsanityData to 7 Oct 2026

Benchmarking Domain Generalization Algorithms in Computational Pathology

arXiv 2409.17063 ↗First posted 25 Sep 2024cs.CV cs.AI cs.LGcited by 3

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Abstract (arXiv)

Deep learning models have shown immense promise in computational pathology (CPath) tasks, but their performance often suffers when applied to unseen data due to domain shifts. Addressing this requires domain generalization (DG) algorithms. However, a systematic evaluation of DG algorithms in the CPath context is lacking. This study aims to benchmark the effectiveness of 30 DG algorithms on 3 CPath tasks of varying difficulty through 7,560 cross-validation runs. We evaluate these algorithms using a unified and robust platform, incorporating modality-specific techniques and recent advances like pretrained foundation models. Our extensive cross-validation experiments provide insights into the relative performance of various DG strategies. We observe that self-supervised learning and stain augmentation consistently outperform other methods, highlighting the potential of pretrained models and data augmentation. Furthermore, we introduce a new pan-cancer tumor detection dataset (HISTOPANTUM) as a benchmark for future research. This study offers valuable guidance to researchers in selecting appropriate DG approaches for CPath tasks.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 45 of its 79 references that are in the map. First posted on arXiv 25 Sep 2024.

Builds on

45 of 79 references.

References · 45

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33 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Distilling foundation models for robust and efficient models in digital pathologycited by 33
  2. 2026The Mean is the Mirage: Entropy-Adaptive Model Merging under Heterogeneous Domain Shifts in Medical Imagingcited by 0
  3. 2026Entropy Minimization without Model Collapse: Mitigating Prediction Bias in Medical Imagingcited by 0