arXiv：cs.AI· Solveig Thrun, Zijun Sun, Suaiba A. Salahuddin, Kristoffer Wickstr{\o}m, Elisabeth Wetzer, Stine Hansen, Robert Jenssen, Michael Kampffmeyer·· 10 小时前
FlexLMV-Net：显式对齐的多视角乳腺癌风险预测框架，支持可变长度筛查历史
Explicitly Aligned Multi-View Breast Cancer Risk Prediction from Variable-Length Screening Histories
AI 导读
FlexLMV-Net 是一个乳腺级纵向多视角框架，联合建模 CC 与 MLO 视角，通过跨视角时序融合和双流注意力块显式对齐纵向特征图，并支持可变数量的既往检查。在 EMBED 与 CSAW-CC 数据集上，该模型在乳腺密度、癌症亚型及人口统计亚组上均持续优于现有方法。团队同时推出开源基准，统一了单时点与多时点乳腺癌风险预测模型的预处理、训练、推理与评估协议。
正文
Abstract:Accurate breast cancer risk prediction from screening mammography is essential for improved risk stratification and personalized screening. It can benefit from both temporal breast tissue changes across longitudinal examinations and complementary anatomical information from craniocaudal (CC) and mediolateral oblique (MLO) views. Leveraging longitudinal information requires identifying corresponding tissue regions across examinations, yet most approaches rely on implicit feature alignment without explicitly enforcing spatial consistency. Recent work has demonstrated the benefits of explicit temporal alignment; however, existing methods model each view independently, preventing them from exploiting complementary CC and MLO information. Furthermore, explicit approaches have been limited to a single prior examination. To fill these gaps, we propose (this http URL), a breast-wise longitudinal multi-view framework that jointly models CC and MLO views across screening examinations while accommodating a variable number of prior examinations. FlexLMV-Net explicitly aligns longitudinal feature maps and integrates complementary views through cross-view temporal fusion and a dual-stream attention block. Evaluated on EMBED and CSAW-CC, FlexLMV-Net consistently outperforms existing approaches across breast density, cancer subtypes, and demographic subgroups, highlighting the benefits of explicit longitudinal alignment, multi-view modeling, and flexible use of available screening history. We further introduce (this http URL), an open-source benchmark that unifies state-of-the-art single- and multi-timepoint breast cancer risk prediction models under standardized preprocessing, training, inference, and evaluation protocols, enabling fair and reproducible comparison of existing methods.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.16329 [cs.CV] |
| (or arXiv:2610.16329v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.16329 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Solveig Thrun [view email]
[v1]
Mon, 12 Oct 2026 07:47:42 UTC (16,176 KB)
来源：arXiv：cs.AI · arxiv.org