Transfer Learning Strategies for Pathological Foundation Models: A Systematic Evaluation in Brain Tumor Classification
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908306445959168 |
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| author | Enda, Ken Oda, Yoshitaka Tanei, Zen-ichi Satoh, Kenichi Motegi, Hiroaki Shunsuke, Terasaka Yamaguchi, Shigeru Ogawa, Takahiro Lei, Wang Tsuda, Masumi Tanaka, Shinya |
| author_facet | Enda, Ken Oda, Yoshitaka Tanei, Zen-ichi Satoh, Kenichi Motegi, Hiroaki Shunsuke, Terasaka Yamaguchi, Shigeru Ogawa, Takahiro Lei, Wang Tsuda, Masumi Tanaka, Shinya |
| contents | Foundation models pretrained on large-scale pathology datasets have shown promising results across various diagnostic tasks. Here, we present a systematic evaluation of transfer learning strategies for brain tumor classification using these models. We analyzed 254 cases comprising five major tumor types: glioblastoma, astrocytoma, oligodendroglioma, primary central nervous system lymphoma, and metastatic tumors. Comparing state-of-the-art foundation models with conventional approaches, we found that foundation models demonstrated robust classification performance with as few as 10 patches per case, despite the traditional assumption that extensive per-case image sampling is necessary. Furthermore, our evaluation revealed that simple transfer learning strategies like linear probing were sufficient, while fine-tuning often degraded model performance. These findings suggest a paradigm shift from "training encoders on extensive pathological data" to "querying pre-trained encoders with labeled datasets", providing practical implications for implementing AI-assisted diagnosis in clinical pathology. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2501_11014 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Transfer Learning Strategies for Pathological Foundation Models: A Systematic Evaluation in Brain Tumor Classification Enda, Ken Oda, Yoshitaka Tanei, Zen-ichi Satoh, Kenichi Motegi, Hiroaki Shunsuke, Terasaka Yamaguchi, Shigeru Ogawa, Takahiro Lei, Wang Tsuda, Masumi Tanaka, Shinya Image and Video Processing Computer Vision and Pattern Recognition 62M45, 62P10, 68T07 I.2.6; I.5.4; J.3 Foundation models pretrained on large-scale pathology datasets have shown promising results across various diagnostic tasks. Here, we present a systematic evaluation of transfer learning strategies for brain tumor classification using these models. We analyzed 254 cases comprising five major tumor types: glioblastoma, astrocytoma, oligodendroglioma, primary central nervous system lymphoma, and metastatic tumors. Comparing state-of-the-art foundation models with conventional approaches, we found that foundation models demonstrated robust classification performance with as few as 10 patches per case, despite the traditional assumption that extensive per-case image sampling is necessary. Furthermore, our evaluation revealed that simple transfer learning strategies like linear probing were sufficient, while fine-tuning often degraded model performance. These findings suggest a paradigm shift from "training encoders on extensive pathological data" to "querying pre-trained encoders with labeled datasets", providing practical implications for implementing AI-assisted diagnosis in clinical pathology. |
| title | Transfer Learning Strategies for Pathological Foundation Models: A Systematic Evaluation in Brain Tumor Classification |
| topic | Image and Video Processing Computer Vision and Pattern Recognition 62M45, 62P10, 68T07 I.2.6; I.5.4; J.3 |
| url | https://arxiv.org/abs/2501.11014 |