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

NeurIPS 2025 Datasets and Benchmarks Track (Spotlight)

[arxiv] [code] [docs]

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.

@article{marza2025thunder,
  title={THUNDER: Tile-level Histopathology image UNDERstanding benchmark},
  author={Marza, Pierre and Fillioux, Leo and Boutaj, Sofi{\`e}ne and Mahatha, Kunal and Desrosiers, Christian and Piantanida, Pablo and Dolz, Jose and Christodoulidis, Stergios and Vakalopoulou, Maria},
  journal={Neural Information Processing Systems (NeurIPS) D&B Track},
  year={2025}
}