Detection of Breast Cancer Lumpectomy Margin with SAM-incorporated Forward-Forward Contrastive Learning
Journal:
arXiv
Published Date:
Jun 26, 2025
Abstract
Complete removal of cancer tumors with a negative specimen margin during
lumpectomy is essential in reducing breast cancer recurrence. However, 2D
specimen radiography (SR), the current method used to assess intraoperative
specimen margin status, has limited accuracy, resulting in nearly a quarter of
patients requiring additional surgery. To address this, we propose a novel deep
learning framework combining the Segment Anything Model (SAM) with
Forward-Forward Contrastive Learning (FFCL), a pre-training strategy leveraging
both local and global contrastive learning for patch-level classification of SR
images. After annotating SR images with regions of known maligancy,
non-malignant tissue, and pathology-confirmed margins, we pre-train a ResNet-18
backbone with FFCL to classify margin status, then reconstruct coarse binary
masks to prompt SAM for refined tumor margin segmentation. Our approach
achieved an AUC of 0.8455 for margin classification and segmented margins with
a 27.4% improvement in Dice similarity over baseline models, while reducing
inference time to 47 milliseconds per image. These results demonstrate that
FFCL-SAM significantly enhances both the speed and accuracy of intraoperative
margin assessment, with strong potential to reduce re-excision rates and
improve surgical outcomes in breast cancer treatment. Our code is available at
https://github.com/tbwa233/FFCL-SAM/.
