{"ID":2896928,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.05742","arxiv_id":"2507.05742","title":"Whole Slide Concepts: A Supervised Foundation Model For Pathological Images","abstract":"Foundation models (FMs) are transforming computational pathology by offering new ways to analyze histopathology images. However, FMs typically require weeks of training on large databases, making their creation a resource-intensive process. In this paper, we present a training for foundation models from whole slide images using supervised, end-to-end, multitask learning on slide-level labels. Notably, it is the first model to incorporate cancer subtyping, risk estimation, and genetic mutation prediction into one model. The presented model outperforms self-supervised models on seven benchmark tasks while the training only required 5% of the computational resources. The results not only show that supervised training can outperform self-supervision with less data, but also offer a solution to annotation problems, as patient-based labels are widely available through routine clinical processes. Furthermore, an attention module provides a layer of explainability across different tasks and serves as a tumor detector for unseen cancer types. To address the issue of closed-source datasets, the model was fully trained on openly available data. The code and model weights are made available under https://github.com/FraunhoferMEVIS/MedicalMultitaskModeling.","short_abstract":"Foundation models (FMs) are transforming computational pathology by offering new ways to analyze histopathology images. However, FMs typically require weeks of training on large databases, making their creation a resource-intensive process. In this paper, we present a training for foundation models from whole slide ima...","url_abs":"https://arxiv.org/abs/2507.05742","url_pdf":"https://arxiv.org/pdf/2507.05742v3","authors":"[\"Till Nicke\",\"Daniela Schacherer\",\"Jan Raphael Schäfer\",\"Natalia Artysh\",\"Antje Prasse\",\"André Homeyer\",\"Andrea Schenk\",\"Henning Höfener\",\"Johannes Lotz\"]","published":"2025-07-08T07:42:12Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"cs.CV\"]","methods":"[]","has_code":false,"code_links":[{"ID":612318,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2896928,"paper_url":"https://arxiv.org/abs/2507.05742","paper_title":"Whole Slide Concepts: A Supervised Foundation Model For Pathological Images","repo_url":"https://github.com/FraunhoferMEVIS/MedicalMultitaskModeling","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}