标题： VMRA-MaR：一种面向纵向乳腺癌风险预测的感知不对称性的时间框架 显示英文标题

标题： VMRA-MaR: An Asymmetry-Aware Temporal Framework for Longitudinal Breast Cancer Risk Prediction

摘要： 乳腺癌仍然是全球导致死亡的主要原因之一，通常通过筛查计划进行检测，其中健康人群会被定期邀请参加。自动化风险预测方法有潜力通过促进高风险群体的动态筛查来改进这一过程。尽管大多数模型仅关注最近的筛查，但受临床实践的启发，越来越多的研究兴趣在于利用时间信息来捕捉乳腺组织的演变趋势。早期的方法通常依赖于两个时间步骤，尽管最近的努力使用Transformer架构将这一时间扩展到多个时间步骤，但在充分利用纵向影像数据中固有的丰富时间动态方面仍存在挑战。在本工作中，我们提出改用视觉马尔可夫递归神经网络（VMRNN），结合状态空间模型（SSM）和类似LSTM的记忆机制，以有效捕捉乳腺组织演变中的细微趋势。为进一步增强我们的方法，我们引入了一个不对称模块，该模块利用空间不对称检测器（SAD）和纵向不对称跟踪器（LAT）来识别临床上相关的双侧差异。这个集成框架在预测癌症发生方面表现出显著的改进，特别是在更具挑战性的高密度乳腺病例中，并在延长的时间点（第四年和第五年）实现了优越的性能，突显了其在推进早期乳腺癌识别和实现更个性化的筛查策略方面的潜力。我们的代码可在 https://github.com/Mortal-Suen/VMRA-MaR.git 获取。 显示英文摘要

摘要： Breast cancer remains a leading cause of mortality worldwide and is typically detected via screening programs where healthy people are invited in regular intervals. Automated risk prediction approaches have the potential to improve this process by facilitating dynamically screening of high-risk groups. While most models focus solely on the most recent screening, there is growing interest in exploiting temporal information to capture evolving trends in breast tissue, as inspired by clinical practice. Early methods typically relied on two time steps, and although recent efforts have extended this to multiple time steps using Transformer architectures, challenges remain in fully harnessing the rich temporal dynamics inherent in longitudinal imaging data. In this work, we propose to instead leverage Vision Mamba RNN (VMRNN) with a state-space model (SSM) and LSTM-like memory mechanisms to effectively capture nuanced trends in breast tissue evolution. To further enhance our approach, we incorporate an asymmetry module that utilizes a Spatial Asymmetry Detector (SAD) and Longitudinal Asymmetry Tracker (LAT) to identify clinically relevant bilateral differences. This integrated framework demonstrates notable improvements in predicting cancer onset, especially for the more challenging high-density breast cases and achieves superior performance at extended time points (years four and five), highlighting its potential to advance early breast cancer recognition and enable more personalized screening strategies. Our code is available at https://github.com/Mortal-Suen/VMRA-MaR.git.