THUNDER: Tile-level Histopathology image UNDERstanding benchmark

Pierre Marza; Leo Fillioux; Sofiène Boutaj; Kunal Mahatha; Christian Desrosiers; Pablo Piantanida; Jose Dolz; Stergios Christodoulidis; Maria Vakalopoulou

arXiv · 2507.07860

THUNDER: Tile-level Histopathology image UNDERstanding benchmark

Pierre Marza·Leo Fillioux·Sofiène Boutaj·Kunal Mahatha·Christian Desrosiers·Pablo Piantanida·Jose Dolz·Stergios Christodoulidis·Maria Vakalopoulou

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Abstract

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.

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Pierre Marza, Leo Fillioux, Sofiène Boutaj, Kunal Mahatha, Christian Desrosiers, Pablo Piantanida, Jose Dolz, Stergios Christodoulidis, Maria Vakalopoulou. 2026-02-17. THUNDER: Tile-level Histopathology image UNDERstanding benchmark. https://arxiv.org/abs/2507.07860

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