利用双侧与时间维度的不对称性，提升乳腺癌风险预测精度。
STA-Risk: A Deep Dive of Spatio-Temporal Asymmetries for Breast Cancer Risk Prediction
- 基于Transformer构建模型，同时捕捉双侧与纵向影像差异
- 在1-5年风险预测中超越4种顶尖模型表现
- 适合临床早期干预与个性化筛查策略制定
预测乳腺癌发病风险是指导早期干预和个性化筛查的重要工具。早期风险模型性能有限，尽管近期基于机器学习的乳腺钼靶图像分析展现出良好预测效果，但这些模型多仅依赖单次检查，或忽视长期影像中乳腺组织细微的空间-时间演变特征，而这些特征对风险评估至关重要。本文提出STA-Risk（基于空间-时间不对称性的风险预测）模型，采用Transformer架构，通过双侧编码与时间编码，同步捕捉双侧乳腺及长期影像中的不对称演化模式，并引入定制化的不对称性损失函数进行优化。在两个独立的乳腺钼靶数据集上进行了广泛实验，结果表明，该模型在1至5年未来风险预测任务中显著优于四种代表性先进模型。源代码将在论文发表后公开。
原文摘要 · Abstract (English)
Predicting the risk of developing breast cancer is an important clinical tool to guide early intervention and tailoring personalized screening strategies. Early risk models have limited performance and recently machine learning-based analysis of mammogram images showed encouraging risk prediction effects. These models however are limited to the use of a single exam or tend to overlook nuanced breast tissue evolvement in spatial and temporal details of longitudinal imaging exams that are indicative of breast cancer risk. In this paper, we propose STA-Risk (Spatial and Temporal Asymmetry-based Risk Prediction), a novel Transformer-based model that captures fine-grained mammographic imaging evolution simultaneously from bilateral and longitudinal asymmetries for breast cancer risk prediction. STA-Risk is innovative by the side encoding and temporal encoding to learn spatial-temporal asymmetries, regulated by a customized asymmetry loss. We performed extensive experiments with two independent mammogram datasets and achieved superior performance than four representative SOTA models for 1- to 5-year future risk prediction. Source codes will be released upon publishing of the paper.
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