Vilde Schulerud

• Bøe, Vilde Schulerud; Kleppe, Andreas; Foersch, Sebastian; Wagner, Daniel-Christoph; Busund, Lill-Tove Rasmussen & Rivera, Adín Ramírez (2025). Low-Rank Adaptations for increased Generalization in Foundation Model features. Proceedings of Machine Learning Research (PMLR). doi: https:/openreview.net/pdf?id=0BJTRUVDf4. Fulltekst i vitenarkiv Vis sammendrag

  For foundation models (FMs) to truly advance computational pathology, they must deliver consistent and reliable predictions under diverse, unseen test conditions. Without such robustness, clinical trust and widespread adoption remain out of reach. Although many FMs for histopathology now exist, they have to our knowledge not been systematically tested for robustness by external researchers on independent datasets. In this study, we evaluate the robustness of foundation model features on three separate histopathology datasets and find that their performance drops on external data. Our analysis also reveals that these models often encode dataset-specific information, limiting their generalizability. To address this issue, we train a Weight-Decomposed Low-Rank Adaptation (DoRA) with strong data augmentations to improve feature robustness. Our experiments show that models trained with this adapter exhibit fewer signs of dataset-specific information and may generate more robust features across domains. These results highlight the need for robustness testing and encourage incorporating robustness considerations into the development, training, and tuning of FMs for histopathology. The code for this work will be available at https://github.com/dsb-ifi/DoRA-for-FM-robustness

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• Bøe, Vilde Schulerud (2026). Foundation Models for Pathology and the need for Robust Representations. doi: https:/www.youtube.com/watch?v=7dN0KKxmXrQ&t=2s. Fulltekst i vitenarkiv Vis sammendrag

  Deep learning has transformed computational pathology, enabling highly accurate models for tasks such as survival analysis and tumor detection. However, despite impressive progress, these models often struggle to maintain performance across domain shifts arising from variations in staining, scanners, institutions, and patient populations. This highlights the challenge of robustness and generalization. In this talk, I will discuss emerging efforts to build pathology models that are not only accurate, but also reliable across diverse real-world settings. Focusing on foundation models and multiple-instance learning (MIL), we will look at how sources of dataset-specific biases become embedded in learned representations and how they affect downstream performance. I will present current approaches for improving robustness, including our work on encouraging consistent representations across scanner variations during MIL training. I aim to highlight the progress that has been made and the challenges that remain in developing pathology AI systems that generalize beyond the conditions in which they were trained.

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