Accelerating Data Processing and Benchmarking of AI Models for Pathology
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866929707487854592 |
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| author | Zhang, Andrew Jaume, Guillaume Vaidya, Anurag Ding, Tong Mahmood, Faisal |
| author_facet | Zhang, Andrew Jaume, Guillaume Vaidya, Anurag Ding, Tong Mahmood, Faisal |
| contents | Advances in foundation modeling have reshaped computational pathology. However, the increasing number of available models and lack of standardized benchmarks make it increasingly complex to assess their strengths, limitations, and potential for further development. To address these challenges, we introduce a new suite of software tools for whole-slide image processing, foundation model benchmarking, and curated publicly available tasks. We anticipate that these resources will promote transparency, reproducibility, and continued progress in the field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_06750 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Accelerating Data Processing and Benchmarking of AI Models for Pathology Zhang, Andrew Jaume, Guillaume Vaidya, Anurag Ding, Tong Mahmood, Faisal Computer Vision and Pattern Recognition Advances in foundation modeling have reshaped computational pathology. However, the increasing number of available models and lack of standardized benchmarks make it increasingly complex to assess their strengths, limitations, and potential for further development. To address these challenges, we introduce a new suite of software tools for whole-slide image processing, foundation model benchmarking, and curated publicly available tasks. We anticipate that these resources will promote transparency, reproducibility, and continued progress in the field. |
| title | Accelerating Data Processing and Benchmarking of AI Models for Pathology |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.06750 |