Summary of Intra-operative Tumour Margin Evaluation in Breast-conserving Surgery with Deep Learning, by Wei-chung Shia et al.
Intra-operative tumour margin evaluation in breast-conserving surgery with deep learning
by Wei-Chung Shia, Yu-Len Huang, Yi-Chun Chen, Hwa-Koon Wu, Dar-Ren Chen
First submitted to arxiv on: 16 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes an innovative intra-operative tumour margin evaluation scheme for breast-conserving surgery, aiming to provide real-time information to surgeons on the presence of positive resection margins. To achieve this, the authors utilize specimen mammography and deep learning models, specifically SegNet, to segment tumour tissue and evaluate the margin width of normal tissues surrounding it. The proposed method is evaluated against manually determined contours by experienced physicians and pathology reports, demonstrating a high degree of accuracy with an average difference of 6.53 mm ± 5.84. This technology has the potential to revolutionize intra-operative diagnosis, enabling surgeons to make informed decisions during breast-conserving surgery. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study creates a special tool to help doctors during breast cancer surgery. It uses pictures taken inside the body and computer algorithms to check if there’s any cancer left behind after removing the tumour. The goal is to give doctors the information they need in real-time, so they can make sure all the bad cells are removed. The scientists tested their method on 30 cases and found it worked really well, matching what pathologists saw under a microscope. |
Keywords
» Artificial intelligence » Deep learning
