A Comprehensive Benchmark of Histopathology Foundation Models for Kidney Digital Pathology Images

Harishwar Reddy Kasireddy; Patricio S. La Rosa; Akshita Gupta; Anindya S. Paul; Jamie L. Fermin; William L. Clapp; Meryl A. Waldman; Tarek M. El-Ashkar; Sanjay Jain; Luis Rodrigues; Kuang Yu Jen; Avi Z. Rosenberg; Michael T. Eadon; Jeffrey B. Hodgin; Pinaki Sarder

arXiv · 2603.15967

A Comprehensive Benchmark of Histopathology Foundation Models for Kidney Digital Pathology Images

Harishwar Reddy Kasireddy·Patricio S. La Rosa·Akshita Gupta·Anindya S. Paul·Jamie L. Fermin·William L. Clapp·Meryl A. Waldman·Tarek M. El-Ashkar·Sanjay Jain·Luis Rodrigues·Kuang Yu Jen·Avi Z. Rosenberg·Michael T. Eadon·Jeffrey B. Hodgin·Pinaki Sarder

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Abstract

Histopathology foundation models (HFMs), pretrained on large-scale cancer datasets, have advanced computational pathology. However, their applicability to non-cancerous chronic kidney disease remains underexplored, despite coexistence of renal pathology with malignancies such as renal cell and urothelial carcinoma. We systematically evaluate 11 publicly available HFMs across 11 kidney-specific downstream tasks spanning multiple stains (PAS, H&E, PASM, and IHC), spatial scales (tile and slide-level), task types (classification, regression, and copy detection), and clinical objectives, including detection, diagnosis, and prognosis. Tile-level performance is assessed using repeated stratified group cross-validation, while slide-level tasks are evaluated using repeated nested stratified cross-validation. Statistical significance is examined using Friedman test followed by pairwise Wilcoxon signed-rank testing with Holm-Bonferroni correction and compact letter display visualization. To promote reproducibility, we release an open-source Python package, kidney-hfm-eval, available at https://pypi.org/project/kidney-hfm-eval/ , that reproduces the evaluation pipelines. Results show moderate to strong performance on tasks driven by coarse meso-scale renal morphology, including diagnostic classification and detection of prominent structural alterations. In contrast, performance consistently declines for tasks requiring fine-grained microstructural discrimination, complex biological phenotypes, or slide-level prognostic inference, largely independent of stain type. Overall, current HFMs appear to encode predominantly static meso-scale representations and may have limited capacity to capture subtle renal pathology or prognosis-related signals. Our results highlight the need for kidney-specific, multi-stain, and multimodal foundation models to support clinically reliable decision-making in nephrology.

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Harishwar Reddy Kasireddy, Patricio S. La Rosa, Akshita Gupta, Anindya S. Paul, Jamie L. Fermin, William L. Clapp, Meryl A. Waldman, Tarek M. El-Ashkar, Sanjay Jain, Luis Rodrigues, Kuang Yu Jen, Avi Z. Rosenberg, Michael T. Eadon, Jeffrey B. Hodgin, Pinaki Sarder. 2026-03-18. A Comprehensive Benchmark of Histopathology Foundation Models for Kidney Digital Pathology Images. https://arxiv.org/abs/2603.15967

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