Towards Comprehensive Cellular Characterisation of H&E slides

• URL: http://arxiv.org/abs/2508.09926v3
• Date: Tue, 02 Sep 2025 15:30:58 GMT
• Title: Towards Comprehensive Cellular Characterisation of H&E slides
• Authors: Benjamin Adjadj, Pierre-Antoine Bannier, Guillaume Horent, Sebastien Mandela, Aurore Lyon, Kathryn Schutte, Ulysse Marteau, Valentin Gaury, Laura Dumont, Thomas Mathieu, MOSAIC consortium, Reda Belbahri, Benoît Schmauch, Eric Durand, Katharina Von Loga, Lucie Gillet,
• Abstract summary: HistoPLUS is a state-of-the-art model for cell analysis.<n>It trains on a novel curated pan-cancer dataset of 108,722 nuclei covering 13 cell types.<n>It outperforms current state-of-the-art models in detection quality by 5.2% and overall F1 classification score by 23.7%.
• Score: 0.27993409178463413
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cell detection, segmentation and classification are essential for analyzing tumor microenvironments (TME) on hematoxylin and eosin (H&E) slides. Existing methods suffer from poor performance on understudied cell types (rare or not present in public datasets) and limited cross-domain generalization. To address these shortcomings, we introduce HistoPLUS, a state-of-the-art model for cell analysis, trained on a novel curated pan-cancer dataset of 108,722 nuclei covering 13 cell types. In external validation across 4 independent cohorts, HistoPLUS outperforms current state-of-the-art models in detection quality by 5.2% and overall F1 classification score by 23.7%, while using 5x fewer parameters. Notably, HistoPLUS unlocks the study of 7 understudied cell types and brings significant improvements on 8 of 13 cell types. Moreover, we show that HistoPLUS robustly transfers to two oncology indications unseen during training. To support broader TME biomarker research, we release the model weights and inference code at https://github.com/owkin/histoplus/.

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