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Hypernetwork-Based Adaptive Aggregation for Multimodal Multiple-Instance Learning in Predicting Coronary Calcium Debulking

Kaito Shiku, Ichika Seo, Tetsuya Matoba, Rissei Hino, Yasuhiro Nakano, Ryoma Bise

TL;DR

The paper tackles predicting the need for debulking devices in calcified coronary lesions using preoperative CT, formulating the task as multimodal MIL where a bag of vessel-slice instances is paired with patient tabular data. It introduces HyperAdAgFormer, a hypernetwork-conditioned Transformer that outputs Tabular-conditioned Transformation Parameters $\mathbf{v}^i$ and patient-specific classifier weights $\mathbf{W}^i,\mathbf{b}^i$, enabling adaptive aggregation via the token $\tilde{\mathbf{a}}^i=\mathbf{a}+\mathbf{v}^i$. The approach achieves superior bag-level prediction on a private clinical dataset (AUC $=0.710$, F1 $=0.570$) compared with unimodal MIL and several multimodal baselines, underscoring the value of conditioning aggregation on sparse tabular data. This work offers a practical pathway for preoperative assessment of device necessity and introduces a flexible framework for context-aware MIL in medical imaging.

Abstract

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.

Hypernetwork-Based Adaptive Aggregation for Multimodal Multiple-Instance Learning in Predicting Coronary Calcium Debulking

TL;DR

The paper tackles predicting the need for debulking devices in calcified coronary lesions using preoperative CT, formulating the task as multimodal MIL where a bag of vessel-slice instances is paired with patient tabular data. It introduces HyperAdAgFormer, a hypernetwork-conditioned Transformer that outputs Tabular-conditioned Transformation Parameters and patient-specific classifier weights , enabling adaptive aggregation via the token . The approach achieves superior bag-level prediction on a private clinical dataset (AUC , F1 ) compared with unimodal MIL and several multimodal baselines, underscoring the value of conditioning aggregation on sparse tabular data. This work offers a practical pathway for preoperative assessment of device necessity and introduces a flexible framework for context-aware MIL in medical imaging.

Abstract

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.
Paper Structure (10 sections, 6 equations, 3 figures, 2 tables)

This paper contains 10 sections, 6 equations, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related work
  3. Multimodal MIL for debulking device necessity prediction
  4. Problem setup
  5. Preliminary: transformer base aggregation
  6. Hypernetwork-based Adaptive Aggregation Transformer
  7. Experiments
  8. Conclusion
  9. Compliance with ethical standard
  10. Acknowledgments

Figures (3)

  • Figure 1: (a) Clinical diagnosis of the necessity for using debulking device. (b) Motivation of the proposed tabular data–based adaptive aggregation.
  • Figure 2: Overview of Hypernetwork-based Adaptive Aggregation Transformer (HyperAdAgFormer). The gray arrows indicate the inference flow, while the orange dashed arrows represent the gradient flow to the hypernetwork.
  • Figure 3: Example of instances and their corresponding attention scores estimated by "Feature+Transformer" and the proposed "HyperAdAgFormer."