THUNDER: Tile-level Histopathology image UNDERstanding benchmark
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866910024143470592 |
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| author | Marza, Pierre Fillioux, Leo Boutaj, Sofiène Mahatha, Kunal Desrosiers, Christian Piantanida, Pablo Dolz, Jose Christodoulidis, Stergios Vakalopoulou, Maria |
| author_facet | Marza, Pierre Fillioux, Leo Boutaj, Sofiène Mahatha, Kunal Desrosiers, Christian Piantanida, Pablo Dolz, Jose Christodoulidis, Stergios Vakalopoulou, Maria |
| contents | Progress in a research field can be hard to assess, in particular when many concurrent methods are proposed in a short period of time. This is the case in digital pathology, where many foundation models have been released recently to serve as feature extractors for tile-level images, being used in a variety of downstream tasks, both for tile- and slide-level problems. Benchmarking available methods then becomes paramount to get a clearer view of the research landscape. In particular, in critical domains such as healthcare, a benchmark should not only focus on evaluating downstream performance, but also provide insights about the main differences between methods, and importantly, further consider uncertainty and robustness to ensure a reliable usage of proposed models. For these reasons, we introduce THUNDER, a tile-level benchmark for digital pathology foundation models, allowing for efficient comparison of many models on diverse datasets with a series of downstream tasks, studying their feature spaces and assessing the robustness and uncertainty of predictions informed by their embeddings. THUNDER is a fast, easy-to-use, dynamic benchmark that can already support a large variety of state-of-the-art foundation, as well as local user-defined models for direct tile-based comparison. In this paper, we provide a comprehensive comparison of 23 foundation models on 16 different datasets covering diverse tasks, feature analysis, and robustness. The code for THUNDER is publicly available at https://github.com/MICS-Lab/thunder. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_07860 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | THUNDER: Tile-level Histopathology image UNDERstanding benchmark Marza, Pierre Fillioux, Leo Boutaj, Sofiène Mahatha, Kunal Desrosiers, Christian Piantanida, Pablo Dolz, Jose Christodoulidis, Stergios Vakalopoulou, Maria Computer Vision and Pattern Recognition Progress in a research field can be hard to assess, in particular when many concurrent methods are proposed in a short period of time. This is the case in digital pathology, where many foundation models have been released recently to serve as feature extractors for tile-level images, being used in a variety of downstream tasks, both for tile- and slide-level problems. Benchmarking available methods then becomes paramount to get a clearer view of the research landscape. In particular, in critical domains such as healthcare, a benchmark should not only focus on evaluating downstream performance, but also provide insights about the main differences between methods, and importantly, further consider uncertainty and robustness to ensure a reliable usage of proposed models. For these reasons, we introduce THUNDER, a tile-level benchmark for digital pathology foundation models, allowing for efficient comparison of many models on diverse datasets with a series of downstream tasks, studying their feature spaces and assessing the robustness and uncertainty of predictions informed by their embeddings. THUNDER is a fast, easy-to-use, dynamic benchmark that can already support a large variety of state-of-the-art foundation, as well as local user-defined models for direct tile-based comparison. In this paper, we provide a comprehensive comparison of 23 foundation models on 16 different datasets covering diverse tasks, feature analysis, and robustness. The code for THUNDER is publicly available at https://github.com/MICS-Lab/thunder. |
| title | THUNDER: Tile-level Histopathology image UNDERstanding benchmark |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.07860 |