{"slug": "clinical-feasibility-of-low-magnification-fluorescence-imaging-for-breast-cancer", "title": "Clinical Feasibility of Low-Magnification Fluorescence Imaging for Breast Cancer Margin Detection Using Texture Analysis and Deep Learning", "summary": "A study using microscopy with ultraviolet surface excitation (MUSE) found that 4x magnification achieves the same diagnostic accuracy as 10x for breast cancer margin detection, with deep learning (DL) achieving 96.30% sensitivity, 100% specificity, and 98.18% accuracy at both magnifications. Texture analysis (TA) yielded 96.67% accuracy at both magnifications, with 4x providing better specificity (100% vs 93.33%) and 10x higher sensitivity (100% vs 93.33%). The findings suggest lower magnification can be effectively used in MUSE systems for faster intraoperative margin assessment.", "body_md": "arXiv:2608.11317v1 Announce Type: new\nAbstract: High-resolution images of unprocessed surgical breast tissue can be obtained using microscopy with ultraviolet surface excitation (MUSE). This technique is considered a promising method for checking surgical margins during breast cancer surgery. In this study, MUSE images at 4x and 10x magnifications were compared using patch-level classification methods. Texture analysis (TA) based on local binary patterns (LBP) and deep learning (DL) with a base Vision Transformer (ViT) model were used. Both methods achieved similar performance at both magnifications. Using DL method, both 4x and 10x magnifications achieved 96.30% sensitivity, 100% specificity and 98.18% accuracy. Using TA method, 4x achieved better specificity (100% vs 93.33%) and 10x yielded higher sensitivity (100% vs 93.33%), but both had the same accuracy (96.67%). No clear improvement in performance was observed with 10x magnification. These results show that 4x imaging achieves the same diagnostic accuracy as 10x imaging. At the same time, 4x offers a larger field of view and faster image capture. Therefore, lower magnification can be effectively used in MUSE systems for accurate and efficient intraoperative margin assessment.", "url": "https://wpnews.pro/news/clinical-feasibility-of-low-magnification-fluorescence-imaging-for-breast-cancer", "canonical_source": "https://arxiv.org/abs/2608.11317", "published_at": "2026-08-13 04:00:00+00:00", "updated_at": "2026-08-13 04:12:53.693449+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "computer-vision"], "entities": ["MUSE", "Vision Transformer"], "alternates": {"html": "https://wpnews.pro/news/clinical-feasibility-of-low-magnification-fluorescence-imaging-for-breast-cancer", "markdown": "https://wpnews.pro/news/clinical-feasibility-of-low-magnification-fluorescence-imaging-for-breast-cancer.md", "text": "https://wpnews.pro/news/clinical-feasibility-of-low-magnification-fluorescence-imaging-for-breast-cancer.txt", "jsonld": "https://wpnews.pro/news/clinical-feasibility-of-low-magnification-fluorescence-imaging-for-breast-cancer.jsonld"}}