Paper

{
  "raw_xml": "<entry>\n    <id>http://arxiv.org/abs/2603.15967v1</id>\n    <title>A Comprehensive Benchmark of Histopathology Foundation Models for Kidney Histopathology</title>\n    <updated>2026-03-16T22:37:43Z</updated>\n    <link href='https://arxiv.org/abs/2603.15967v1' rel='alternate' type='text/html'/>\n    <link href='https://arxiv.org/pdf/2603.15967v1' rel='related' title='pdf' type='application/pdf'/>\n    <summary>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&amp;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.</summary>\n    <category scheme='http://arxiv.org/schemas/atom' term='cs.CV'/>\n    <published>2026-03-16T22:37:43Z</published>\n    <arxiv:comment>31 Pages, 14 Tables, 12 figures, Co-correspondence to jhodgin@med.umich.edu and pinaki.sarder@ufl.edu</arxiv:comment>\n    <arxiv:primary_category term='cs.CV'/>\n    <author>\n      <name>Harishwar Reddy Kasireddy</name>\n      <arxiv:affiliation>University of Florida</arxiv:affiliation>\n    </author>\n    <author>\n      <name>Patricio S. La Rosa</name>\n      <arxiv:affiliation>University of Florida</arxiv:affiliation>\n      <arxiv:affiliation>Bayer Company</arxiv:affiliation>\n    </author>\n    <author>\n      <name>Akshita Gupta</name>\n      <arxiv:affiliation>University of Florida</arxiv:affiliation>\n    </author>\n    <author>\n      <name>Anindya S. Paul</name>\n      <arxiv:affiliation>University of Florida</arxiv:affiliation>\n    </author>\n    <author>\n      <name>Jamie L. Fermin</name>\n      <arxiv:affiliation>University of Florida</arxiv:affiliation>\n    </author>\n    <author>\n      <name>William L. Clapp</name>\n      <arxiv:affiliation>University of Florida</arxiv:affiliation>\n    </author>\n    <author>\n      <name>Meryl A. Waldman</name>\n      <arxiv:affiliation>National Institutes of Health</arxiv:affiliation>\n    </author>\n    <author>\n      <name>Tarek M. El-Ashkar</name>\n      <arxiv:affiliation>Indiana University School of Medicine</arxiv:affiliation>\n    </author>\n    <author>\n      <name>Sanjay Jain</name>\n      <arxiv:affiliation>Washington University School of Medicine</arxiv:affiliation>\n    </author>\n    <author>\n      <name>Luis Rodrigues</name>\n      <arxiv:affiliation>Universidade de Coimbra</arxiv:affiliation>\n    </author>\n    <author>\n      <name>Kuang Yu Jen</name>\n      <arxiv:affiliation>University of California Davis</arxiv:affiliation>\n    </author>\n    <author>\n      <name>Avi Z. Rosenberg</name>\n      <arxiv:affiliation>Johns Hopkins University</arxiv:affiliation>\n    </author>\n    <author>\n      <name>Michael T. Eadon</name>\n      <arxiv:affiliation>Indiana University School of Medicine</arxiv:affiliation>\n    </author>\n    <author>\n      <name>Jeffrey B. Hodgin</name>\n      <arxiv:affiliation>University of Michigan</arxiv:affiliation>\n    </author>\n    <author>\n      <name>Pinaki Sarder</name>\n      <arxiv:affiliation>University of Florida</arxiv:affiliation>\n    </author>\n  </entry>"
}