Automatic detection of breast cancer lumpectomy margin from intraoperative specimen mammography

Complete removal of cancer tumors with a negative specimen margin during lumpectomy is essential to reduce breast cancer recurrence. However, 2D radiography, the current method used to assess intraoperative specimen margin status, has limited accuracy, resulting in nearly one in four patients needing repeat surgery. This study aims to develop a deep learning model that improves the detection of positive margins in intraoperative breast lumpectomy specimens on radiographs. We annotated the lumpectomy radiograph images with masking that denotes regions of known malignancy, non-malignant tissue, and the areas of pathology-confirmed positive margin. We propose a pretraining strategy, namely Forward-Forward Contrastive Learning (FFCL) with both local and global-level contrastive learning. Experimental results on our annotated breast radiographs demonstrate the effectiveness of our FFCL method in detecting positive margins from intraoperative radiographs of breast lumpectomy specimens.