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| Natura: | Preprint |
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2026
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| Accesso online: | https://arxiv.org/abs/2603.15967 |
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| author | Kasireddy, Harishwar Reddy La Rosa, Patricio S. Gupta, Akshita Paul, Anindya S. Fermin, Jamie L. Clapp, William L. Waldman, Meryl A. El-Ashkar, Tarek M. Jain, Sanjay Rodrigues, Luis Jen, Kuang Yu Rosenberg, Avi Z. Eadon, Michael T. Hodgin, Jeffrey B. Sarder, Pinaki |
| author_facet | Kasireddy, Harishwar Reddy La Rosa, Patricio S. Gupta, Akshita Paul, Anindya S. Fermin, Jamie L. Clapp, William L. Waldman, Meryl A. El-Ashkar, Tarek M. Jain, Sanjay Rodrigues, Luis Jen, Kuang Yu Rosenberg, Avi Z. Eadon, Michael T. Hodgin, Jeffrey B. Sarder, Pinaki |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_15967 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Comprehensive Benchmark of Histopathology Foundation Models for Kidney Digital Pathology Images Kasireddy, Harishwar Reddy La Rosa, Patricio S. Gupta, Akshita Paul, Anindya S. Fermin, Jamie L. Clapp, William L. Waldman, Meryl A. El-Ashkar, Tarek M. Jain, Sanjay Rodrigues, Luis Jen, Kuang Yu Rosenberg, Avi Z. Eadon, Michael T. Hodgin, Jeffrey B. Sarder, Pinaki Computer Vision and Pattern Recognition 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. |
| title | A Comprehensive Benchmark of Histopathology Foundation Models for Kidney Digital Pathology Images |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.15967 |