Leveraging Foundation Models for Histological Grading in Cutaneous Squamous Cell Carcinoma using PathFMTools

Abdul Rahman Diab, Emily E. Karn, Renchin Wu, Emily S. Ruiz, William Lotter
Proceedings of the Fifth Machine Learning for Health Symposium, PMLR 297:936-951, 2026.

Abstract

Despite the promise of computational pathology foundation models, adapting them to specific clinical tasks remains challenging due to the complexity of whole-slide image ({WSI}) processing, the opacity of learned features, and the wide range of potential adaptation strategies. To address these challenges, we introduce PathFMTools, a lightweight, extensible Python package that enables efficient execution, analysis, and visualization of pathology foundation models. We use this tool to interface with and evaluate two state-of-the-art vision-language foundation models, CONCH and MUSK, on the task of histological grading in cutaneous squamous cell carcinoma ({cSCC}), a critical criterion that informs {cSCC} staging and patient management. Using a cohort of 440 {cSCC} H&E {WSI}s, we benchmark multiple adaptation strategies, demonstrating trade-offs across prediction approaches and validating the potential of using foundation model embeddings to train small specialist models. These findings underscore the promise of pathology foundation models for real-world clinical applications, with PathFMTools enabling efficient analysis and validation.

Cite this Paper

BibTeX
@InProceedings{pmlr-v297-diab26a, title = {Leveraging Foundation Models for Histological Grading in Cutaneous Squamous Cell Carcinoma using PathFMTools}, author = {Diab, Abdul Rahman and Karn, Emily E. and Wu, Renchin and Ruiz, Emily S. and Lotter, William}, booktitle = {Proceedings of the Fifth Machine Learning for Health Symposium}, pages = {936--951}, year = {2026}, editor = {Argaw, Peniel and Zhang, Haoran and Jabbour, Sarah and Chandak, Payal and Ji, Jerry and Mukherjee, Sumit and Salaudeen, Olawale and Chang, Trenton and Healey, Elizabeth and Gröger, Fabian and Adibi, Amin and Hegselmann, Stefan and Wild, Benjamin and Noori, Ayush}, volume = {297}, series = {Proceedings of Machine Learning Research}, month = {13--14 Dec}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v297/main/assets/diab26a/diab26a.pdf}, url = {https://proceedings.mlr.press/v297/diab26a.html}, abstract = {Despite the promise of computational pathology foundation models, adapting them to specific clinical tasks remains challenging due to the complexity of whole-slide image ({WSI}) processing, the opacity of learned features, and the wide range of potential adaptation strategies. To address these challenges, we introduce PathFMTools, a lightweight, extensible Python package that enables efficient execution, analysis, and visualization of pathology foundation models. We use this tool to interface with and evaluate two state-of-the-art vision-language foundation models, CONCH and MUSK, on the task of histological grading in cutaneous squamous cell carcinoma ({cSCC}), a critical criterion that informs {cSCC} staging and patient management. Using a cohort of 440 {cSCC} H&E {WSI}s, we benchmark multiple adaptation strategies, demonstrating trade-offs across prediction approaches and validating the potential of using foundation model embeddings to train small specialist models. These findings underscore the promise of pathology foundation models for real-world clinical applications, with PathFMTools enabling efficient analysis and validation.} }
Endnote
%0 Conference Paper %T Leveraging Foundation Models for Histological Grading in Cutaneous Squamous Cell Carcinoma using PathFMTools %A Abdul Rahman Diab %A Emily E. Karn %A Renchin Wu %A Emily S. Ruiz %A William Lotter %B Proceedings of the Fifth Machine Learning for Health Symposium %C Proceedings of Machine Learning Research %D 2026 %E Peniel Argaw %E Haoran Zhang %E Sarah Jabbour %E Payal Chandak %E Jerry Ji %E Sumit Mukherjee %E Olawale Salaudeen %E Trenton Chang %E Elizabeth Healey %E Fabian Gröger %E Amin Adibi %E Stefan Hegselmann %E Benjamin Wild %E Ayush Noori %F pmlr-v297-diab26a %I PMLR %P 936--951 %U https://proceedings.mlr.press/v297/diab26a.html %V 297 %X Despite the promise of computational pathology foundation models, adapting them to specific clinical tasks remains challenging due to the complexity of whole-slide image ({WSI}) processing, the opacity of learned features, and the wide range of potential adaptation strategies. To address these challenges, we introduce PathFMTools, a lightweight, extensible Python package that enables efficient execution, analysis, and visualization of pathology foundation models. We use this tool to interface with and evaluate two state-of-the-art vision-language foundation models, CONCH and MUSK, on the task of histological grading in cutaneous squamous cell carcinoma ({cSCC}), a critical criterion that informs {cSCC} staging and patient management. Using a cohort of 440 {cSCC} H&E {WSI}s, we benchmark multiple adaptation strategies, demonstrating trade-offs across prediction approaches and validating the potential of using foundation model embeddings to train small specialist models. These findings underscore the promise of pathology foundation models for real-world clinical applications, with PathFMTools enabling efficient analysis and validation.
APA
Diab, A.R., Karn, E.E., Wu, R., Ruiz, E.S. & Lotter, W.. (2026). Leveraging Foundation Models for Histological Grading in Cutaneous Squamous Cell Carcinoma using PathFMTools. Proceedings of the Fifth Machine Learning for Health Symposium, in Proceedings of Machine Learning Research 297:936-951 Available from https://proceedings.mlr.press/v297/diab26a.html.

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