arXiv cs.AI· Farnoush Bayatmakou, Reza Taleei, Milad Amir Toutounchian, Arash Mohammadi·· 11 小时前AI 评分27
MSMV-Swin：多尺度多视角 Swin Transformer 框架用于乳腺癌诊断
Integrating AI for Human-Centric Breast Cancer Diagnostics: A Multi-Scale and Multi-View Swin Transformer Framework
AI 导读
研究提出 MSMV-Swin，一个基于 Swin Transformer 的多尺度、多视角混合框架，作为辅助放射科医生分析多视角乳腺钼靶的决策支持工具。该框架借助 Segment Anything Model（SAM）分离乳腺区域以降低背景噪声，并采用混合融合结构应对临床中常见的单视角缺失情况。多尺度设计同时捕捉肿瘤局部区域与周边组织的空间特征，使输出更贴近放射科医生的阅片方式。
正文 · 原文
Abstract:Despite advancements in Computer-Aided Diagnosis (CAD) systems, breast cancer remains one of the leading causes of cancer-related deaths among women worldwide. Recent breakthroughs in Artificial Intelligence (AI) have shown significant promise in development of advanced Deep Learning (DL) architectures for breast cancer diagnosis through mammography. In this context, the paper focuses on the integration of AI within a Human-Centric workflow to enhance breast cancer diagnostics. Key challenges are, however, largely overlooked such as reliance on detailed tumor annotations and susceptibility to missing views, particularly during test time. To address these issues, we propose a hybrid, multi-scale and multi-view Swin Transformer-based framework (MSMV-Swin) that enhances diagnostic robustness and accuracy. The proposed MSMV-Swin framework is designed to work as a decision-support tool, helping radiologists analyze multi-view mammograms more effectively. More specifically, the MSMV-Swin framework leverages the Segment Anything Model (SAM) to isolate the breast lobe, reducing background noise and enabling comprehensive feature extraction. The multi-scale nature of the proposed MSMV-Swin framework accounts for tumor-specific regions as well as the spatial characteristics of tissues surrounding the tumor, capturing both localized and contextual information. The integration of contextual and localized data ensures that MSMV-Swin's outputs align with the way radiologists interpret mammograms, fostering better human-AI interaction and trust. A hybrid fusion structure is then designed to ensure robustness against missing views, a common occurrence in clinical practice when only a single mammogram view is available.
| Subjects: | Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2503.13309 [eess.IV] |
| (or arXiv:2503.13309v3 [eess.IV] for this version) | |
| https://doi.org/10.48550/arXiv.2503.13309 arXiv-issued DOI via DataCite |
Submission history
From: Arash Mohammadi [view email]
[v1]
Mon, 17 Mar 2025 15:48:56 UTC (2,423 KB)
[v2]
Wed, 7 May 2025 18:17:50 UTC (2,425 KB)
[v3]
Fri, 30 Oct 2026 16:51:27 UTC (888 KB)
来源：arXiv cs.AI · arxiv.org