纵向多视图乳腺癌风险预测

中文摘要 AI 辅助

从乳腺钼靶筛查中准确预测乳腺癌风险对于实现个性化筛查间隔和早期检测至关重要。近期深度学习方法显示了纵向数据和显式时间对齐的价值。然而，现有方法要么使用单一乳腺钼靶视图进行显式对齐，要么对多个视图建模但无显式纵向对齐，限制了利用临床实践中互补时空信息的能力。为解决这一差距，我们提出LMV-Net，一个纵向多视图乳腺癌风险预测模型，在显式对齐的纵向框架内联合分析解剖互补的CC和MLO视图。我们在公开的EMBED和CSAW-CC数据集上评估该方法，并与最先进的乳腺癌风险预测方法比较。我们的模型在总体风险预测性能以及不同乳腺密度和癌症亚组中始终优于现有方法。重要的是，这些改进突出了纵向多视图建模增强风险分层的潜力，为个性化筛查、高危患者的早期识别和更有效的筛查资源分配的未来工作铺平了道路。代码可在该https网址获取。

英文摘要

Accurate breast cancer risk prediction from screening mammography is critical for enabling personalized screening intervals and early detection. Recent deep learning methods have shown the value of longitudinal data and explicit temporal alignment. However, existing approaches either perform explicit alignment using a single mammographic view or model multiple views without explicit longitudinal alignment, limiting their ability to exploit the complementary spatial-temporal information used in clinical practice. To address this gap, we propose LMV-Net, a longitudinal multi-view breast cancer risk prediction model that jointly analyzes anatomically complementary CC and MLO views within an explicitly aligned longitudinal framework. We evaluate our approach on the public EMBED and CSAW-CC datasets, comparing it to state-of-the-art breast cancer risk prediction methods. Our model consistently outperforms existing approaches in overall risk prediction performance and across different breast density and cancer subgroups. Importantly, these improvements highlight the potential of longitudinal multi-view modeling to enhance risk stratification, paving the way for future work on personalized screening, earlier identification of high-risk patients, and more efficient screening resource allocation. The code is available at https://github.com/sot176/LMV-Net.

发表机构

• UiT The Arctic University of Norway（挪威北极大学UIT校区）
• University Hospital of North Norway（挪威北海岸大学医院）
• Norwegian Computing Center（挪威计算中心）
• University of Copenhagen（哥本哈根大学）

机构由 AI 辅助整理，请以论文原文为准。

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Accepted at MICCAI 2026