基于表格数据自适应调整影像特征聚合，提升冠脉钙化减容必要性预测精度
Hypernetwork-Based Adaptive Aggregation for Multimodal Multiple-Instance Learning in Predicting Coronary Calcium Debulking
- 用超网络动态调节每名患者的特征聚合方式
- 在临床数据集上显著优于传统方法，提升预测准确性
- 适合心血管影像分析与多模态学习研究者参考
本文首次尝试从计算机断层扫描（CT）图像中估计冠状动脉钙化减容的必要性。我们将该任务建模为多实例学习（MIL）问题。难点在于医生会根据患者表格式健康数据调整决策标准和关注重点。为此，我们提出超网络自适应聚合变换器（HyperAdAgFormer），通过超网络根据表格式数据动态调整每位患者的特征聚合策略。在临床数据集上的实验验证了该方法的有效性。代码已公开于 https://github.com/Shiku-Kaito/HyperAdAgFormer。
原文摘要 · Abstract (English)
In this paper, we present the first attempt to estimate the necessity of debulking coronary artery calcifications from computed tomography (CT) images. We formulate this task as a Multiple-instance Learning (MIL) problem. The difficulty of this task lies in that physicians adjust their focus and decision criteria for device usage according to tabular data representing each patient's condition. To address this issue, we propose a hypernetwork-based adaptive aggregation transformer (HyperAdAgFormer), which adaptively modifies the feature aggregation strategy for each patient based on tabular data through a hypernetwork. The experiments using the clinical dataset demonstrated the effectiveness of HyperAdAgFormer. The code is publicly available at https://github.com/Shiku-Kaito/HyperAdAgFormer.
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