{"ID":2829094,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.13608","arxiv_id":"2512.13608","title":"DBT-DINO: Towards Foundation model based analysis of Digital Breast Tomosynthesis","abstract":"Foundation models have shown promise in medical imaging but remain underexplored for three-dimensional imaging modalities. No foundation model currently exists for Digital Breast Tomosynthesis (DBT), despite its use for breast cancer screening. To develop and evaluate a foundation model for DBT (DBT-DINO) across multiple clinical tasks and assess the impact of domain-specific pre-training. Self-supervised pre-training was performed using the DINOv2 methodology on over 25 million 2D slices from 487,975 DBT volumes from 27,990 patients. Three downstream tasks were evaluated: (1) breast density classification using 5,000 screening exams; (2) 5-year risk of developing breast cancer using 106,417 screening exams; and (3) lesion detection using 393 annotated volumes. For breast density classification, DBT-DINO achieved an accuracy of 0.79 (95\\% CI: 0.76--0.81), outperforming both the MetaAI DINOv2 baseline (0.73, 95\\% CI: 0.70--0.76, p\u003c.001) and DenseNet-121 (0.74, 95\\% CI: 0.71--0.76, p\u003c.001). For 5-year breast cancer risk prediction, DBT-DINO achieved an AUROC of 0.78 (95\\% CI: 0.76--0.80) compared to DINOv2's 0.76 (95\\% CI: 0.74--0.78, p=.57). For lesion detection, DINOv2 achieved a higher average sensitivity of 0.67 (95\\% CI: 0.60--0.74) compared to DBT-DINO with 0.62 (95\\% CI: 0.53--0.71, p=.60). DBT-DINO demonstrated better performance on cancerous lesions specifically with a detection rate of 78.8\\% compared to Dinov2's 77.3\\%. Using a dataset of unprecedented size, we developed DBT-DINO, the first foundation model for DBT. DBT-DINO demonstrated strong performance on breast density classification and cancer risk prediction. However, domain-specific pre-training showed variable benefits on the detection task, with ImageNet baseline outperforming DBT-DINO on general lesion detection, indicating that localized detection tasks require further methodological development.","short_abstract":"Foundation models have shown promise in medical imaging but remain underexplored for three-dimensional imaging modalities. No foundation model currently exists for Digital Breast Tomosynthesis (DBT), despite its use for breast cancer screening. To develop and evaluate a foundation model for DBT (DBT-DINO) across multip...","url_abs":"https://arxiv.org/abs/2512.13608","url_pdf":"https://arxiv.org/pdf/2512.13608v1","authors":"[\"Felix J. Dorfner\",\"Manon A. Dorster\",\"Ryan Connolly\",\"Oscar Gentilhomme\",\"Edward Gibbs\",\"Steven Graham\",\"Seth Wander\",\"Thomas Schultz\",\"Manisha Bahl\",\"Dania Daye\",\"Albert E. Kim\",\"Christopher P. Bridge\"]","published":"2025-12-15T18:03:09Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}