Lesion-Aware Attention for Mammography Segmentation with Heterogeneous Supervision
Accurate lesion segmentation in mammography is important for computer-aided breast cancer diagnosis, but dense pixel-level annotations remain costly and limited. Existing public segmentation datasets usually cover only a small set of lesion types with manually delineated masks, whereas larger mammography datasets often provide only image-level diagnostic labels. This annotation mismatch makes it difficult to train segmentation models that can generalize to rare or weakly annotated lesion categories. To address this problem, we study open-vocabulary mammographic lesion segmentation under a mixed-supervision setting, where pixel-level masks are available for base lesion categories and image...
| Authors | Di Dai, Bo Liu, Liwei Wang |
|---|---|
| Primary category | cs.CV |
| Published | 11 October 2026 |
| Last updated at source | 11 October 2026 |
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