Unveiling hidden risks: A Holistically-Driven Weak Supervision framework for ultra-short-term ACS prediction using CCTA

IF 4.9 2区医学 Q1 ENGINEERING, BIOMEDICAL

Computerized Medical Imaging and Graphics Pub Date : 2025-09-15 DOI:10.1016/j.compmedimag.2025.102636

Zhen Liu , Bangkang Fu , Jiahui Mao , Junjie He , Jiangyue Xiang , Hongjin Li , Yunsong Peng , Bangguo Li , Rongpin Wang

引用次数: 0

Abstract

This paper proposes MH-STR, a novel end-to-end framework for predicting the three-month risk of Acute Coronary Syndrome (ACS) from Coronary CT Angiography (CCTA) images. The model combines hybrid attention mechanisms with convolutional networks to capture subtle and irregular lesion patterns that are difficult to detect visually. A stage-wise transfer learning strategy helps distill general features and transfer vascular-specific knowledge. To reconcile feature scale mismatches in the dual-branch architecture, we introduce a wavelet-based multi-scale fusion module for effective integration across scales. Experiments show that MH-STR achieves an AUC of 0.834, an F1 score of 0.82, and a precision of 0.92, outperforming existing methods and highlighting its potential for improving ACS risk prediction.

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揭示隐藏的风险：使用CCTA进行超短期ACS预测的整体驱动弱监管框架。

本文提出了MH-STR，一个新的端到端框架，用于从冠状动脉CT血管造影（CCTA）图像预测三个月的急性冠脉综合征（ACS）风险。该模型将混合注意机制与卷积网络相结合，以捕获难以视觉检测的细微和不规则病变模式。分阶段迁移学习策略有助于提取一般特征并迁移血管特定知识。为了解决双分支结构中特征尺度不匹配的问题，我们引入了基于小波的多尺度融合模块，实现了多尺度的有效融合。实验结果表明，MH-STR的AUC为0.834，F1分数为0.82，精度为0.92，优于现有方法，具有提高ACS风险预测的潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computerized Medical Imaging and Graphics 医学-核医学

CiteScore

10.70

自引率

3.50%

发文量

审稿时长

26 days

期刊介绍： The purpose of the journal Computerized Medical Imaging and Graphics is to act as a source for the exchange of research results concerning algorithmic advances, development, and application of digital imaging in disease detection, diagnosis, intervention, prevention, precision medicine, and population health. Included in the journal will be articles on novel computerized imaging or visualization techniques, including artificial intelligence and machine learning, augmented reality for surgical planning and guidance, big biomedical data visualization, computer-aided diagnosis, computerized-robotic surgery, image-guided therapy, imaging scanning and reconstruction, mobile and tele-imaging, radiomics, and imaging integration and modeling with other information relevant to digital health. The types of biomedical imaging include: magnetic resonance, computed tomography, ultrasound, nuclear medicine, X-ray, microwave, optical and multi-photon microscopy, video and sensory imaging, and the convergence of biomedical images with other non-imaging datasets.