Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.03532 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916881722507264 |
|---|---|
| author | Winklmayr, Claudia Luescher, Jerome Koreuber, Nora Franzen, Jannik Reith, Fabian H. Baumann, Elias Schuerch, Christian M. Kainmueller, Dagmar Rumberger, Josef Lorenz |
| author_facet | Winklmayr, Claudia Luescher, Jerome Koreuber, Nora Franzen, Jannik Reith, Fabian H. Baumann, Elias Schuerch, Christian M. Kainmueller, Dagmar Rumberger, Josef Lorenz |
| contents | Digital pathology has seen the advent of a wealth of foundational models (FM), yet to date their performance on cell phenotyping has not been benchmarked in a unified manner. We therefore propose PhenoBench: A comprehensive benchmark for cell phenotyping on Hematoxylin and Eosin (H&E) stained histopathology images. We provide both PhenoCell, a new H&E dataset featuring 14 granular cell types identified by using multiplexed imaging, and ready-to-use fine-tuning and benchmarking code that allows the systematic evaluation of multiple prominent pathology FMs in terms of dense cell phenotype predictions in different generalization scenarios. We perform extensive benchmarking of existing FMs, providing insights into their generalization behavior under technical vs. medical domain shifts. Furthermore, while FMs achieve macro F1 scores > 0.70 on previously established benchmarks such as Lizard and PanNuke, on PhenoCell, we observe scores as low as 0.20. This indicates a much more challenging task not captured by previous benchmarks, establishing PhenoCell as a prime asset for future benchmarking of FMs and supervised models alike. Code and data are available on GitHub. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_03532 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PhenoBench: A Comprehensive Benchmark for Cell Phenotyping Winklmayr, Claudia Luescher, Jerome Koreuber, Nora Franzen, Jannik Reith, Fabian H. Baumann, Elias Schuerch, Christian M. Kainmueller, Dagmar Rumberger, Josef Lorenz Computer Vision and Pattern Recognition Digital pathology has seen the advent of a wealth of foundational models (FM), yet to date their performance on cell phenotyping has not been benchmarked in a unified manner. We therefore propose PhenoBench: A comprehensive benchmark for cell phenotyping on Hematoxylin and Eosin (H&E) stained histopathology images. We provide both PhenoCell, a new H&E dataset featuring 14 granular cell types identified by using multiplexed imaging, and ready-to-use fine-tuning and benchmarking code that allows the systematic evaluation of multiple prominent pathology FMs in terms of dense cell phenotype predictions in different generalization scenarios. We perform extensive benchmarking of existing FMs, providing insights into their generalization behavior under technical vs. medical domain shifts. Furthermore, while FMs achieve macro F1 scores > 0.70 on previously established benchmarks such as Lizard and PanNuke, on PhenoCell, we observe scores as low as 0.20. This indicates a much more challenging task not captured by previous benchmarks, establishing PhenoCell as a prime asset for future benchmarking of FMs and supervised models alike. Code and data are available on GitHub. |
| title | PhenoBench: A Comprehensive Benchmark for Cell Phenotyping |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.03532 |