{"ID":2875584,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.02710","arxiv_id":"2509.02710","title":"Toward a robust lesion detection model in breast DCE-MRI: adapting foundation models to high-risk women","abstract":"Accurate breast MRI lesion detection is critical for early cancer diagnosis, especially in high-risk populations. We present a classification pipeline that adapts a pretrained foundation model, the Medical Slice Transformer (MST), for breast lesion classification using dynamic contrast-enhanced MRI (DCE-MRI). Leveraging DINOv2-based self-supervised pretraining, MST generates robust per-slice feature embeddings, which are then used to train a Kolmogorov--Arnold Network (KAN) classifier. The KAN provides a flexible and interpretable alternative to conventional convolutional networks by enabling localized nonlinear transformations via adaptive B-spline activations. This enhances the model's ability to differentiate benign from malignant lesions in imbalanced and heterogeneous clinical datasets. Experimental results demonstrate that the MST+KAN pipeline outperforms the baseline MST classifier, achieving AUC = 0.80 \\pm 0.02 while preserving interpretability through attention-based heatmaps. Our findings highlight the effectiveness of combining foundation model embeddings with advanced classification strategies for building robust and generalizable breast MRI analysis tools.","short_abstract":"Accurate breast MRI lesion detection is critical for early cancer diagnosis, especially in high-risk populations. We present a classification pipeline that adapts a pretrained foundation model, the Medical Slice Transformer (MST), for breast lesion classification using dynamic contrast-enhanced MRI (DCE-MRI). Leveragin...","url_abs":"https://arxiv.org/abs/2509.02710","url_pdf":"https://arxiv.org/pdf/2509.02710v1","authors":"[\"Gabriel A. B. do Nascimento\",\"Vincent Dong\",\"Guilherme J. Cavalcante\",\"Alex Nguyen\",\"Thaís G. do Rêgo\",\"Yuri Malheiros\",\"Telmo M. Silva Filho\",\"Carla R. Zeballos Torrez\",\"James C. Gee\",\"Anne Marie McCarthy\",\"Andrew D. A. Maidment\",\"Bruno Barufaldi\"]","published":"2025-09-02T18:11:18Z","proceeding":"physics.med-ph","tasks":"[\"physics.med-ph\",\"cs.CV\",\"cs.LG\"]","methods":"[\"Transformer\"]","has_code":false}