Lesion-Aware Attention for Mammography Segmentation with Heterogeneous Supervision

Key points

• 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-level labels are available for a broader set of categories.1
• We propose a two-branch framework that jointly learns from segmentation data and classification data.1
• The segmentation branch learns lesion boundaries from annotated masks, while the classification branch introduces additional image-level semantic supervision.1
• To better transfer image-level supervision to lesion-relevant regions, we further introduce a Lesion-wise Attentive Classifier that maintains category-specific feature prototypes and generates lesion-aware attention maps for foreground feature pooling.1

Sources (1)

• [1]Lesion-Aware Attention for Mammography Segmentation with Heterogeneous Supervision
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 11, 01:36 PM

  Accurate lesion segmentation in mammography is important for computer-aided breast cancer diagnosis, but dense pixel-level annotations remain costly and limited.

  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-level labels are available for a broader set of categories.

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