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.

翻译：本文首次尝试从计算机断层扫描（CT）图像中评估冠状动脉钙化减容的必要性。我们将此任务形式化为一个多示例学习（MIL）问题。该任务的难点在于，医生会根据代表每位患者状况的表格数据调整其关注点和设备使用的决策标准。为解决此问题，我们提出了一种基于超网络的自适应聚合Transformer（HyperAdAgFormer），它通过超网络根据表格数据自适应地修改每位患者的特征聚合策略。使用临床数据集的实验证明了HyperAdAgFormer的有效性。代码公开于 https://github.com/Shiku-Kaito/HyperAdAgFormer。