Accelerating Data Processing and Benchmarking of AI Models for Pathology

Andrew Zhang, Guillaume Jaume, Anurag Vaidya, Tong Ding, Faisal Mahmood
2/10/2025
cs.CV

No official code found. Best implementation: mahmoodlab/TRIDENT (602 stars, last commit 2026-04-21). 1 more below.

Abstract

Advances in foundation modeling have reshaped computational pathology. However, the increasing number of available models and lack of standardized benchmarks make it increasingly complex to assess their strengths, limitations, and potential for further development. To address these challenges, we introduce a new suite of software tools for whole-slide image processing, foundation model benchmarking, and curated publicly available tasks. We anticipate that these resources will promote transparency, reproducibility, and continued progress in the field.

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Code Implementations(2)

mahmoodlab/TRIDENTCommunity100%

Toolkit for large-scale whole-slide image processing.

602134Python, Jupyter NotebookFeb 10, 20256 months agoNOASSERTION
deep-learningfoundation-modelfoundation-models-for-pathologyhistologypathology+1 more

Standardized benchmark for computational pathology foundation models.

15230Shell, PythonJan 29, 202511 months agoNOASSERTION

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Cite this paper

@article{zhang2025accelerating,
  title  = {Accelerating Data Processing and Benchmarking of AI Models for Pathology},
  author = {Andrew Zhang and Guillaume Jaume and Anurag Vaidya and Tong Ding and Faisal Mahmood},
  year   = {2025},
  eprint = {2502.06750},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2502.06750v1}
}

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