Coronary Calcium Detection using 3D Attention Identical Dual Deep Network Based on Weakly Supervised Learning

TL;DR

This paper introduces AID-Net, a 3D attention-based deep learning model that detects coronary artery calcium from CT scans using weak supervision, reducing manual effort and improving accuracy.

Contribution

The paper presents a novel 3D attention identical dual network with weakly supervised learning and interpretability via Grad-CAM for CAC detection.

Findings

Achieved 92.72% classification accuracy

Attained 0.9627 AUC in detection tasks

Demonstrated superior performance over baseline models

Abstract

Coronary artery calcium (CAC) is biomarker of advanced subclinical coronary artery disease and predicts myocardial infarction and death prior to age 60 years. The slice-wise manual delineation has been regarded as the gold standard of coronary calcium detection. However, manual efforts are time and resource consuming and even impracticable to be applied on large-scale cohorts. In this paper, we propose the attention identical dual network (AID-Net) to perform CAC detection using scan-rescan longitudinal non-contrast CT scans with weakly supervised attention by only using per scan level labels. To leverage the performance, 3D attention mechanisms were integrated into the AID-Net to provide complementary information for classification tasks. Moreover, the 3D Gradient-weighted Class Activation Mapping (Grad-CAM) was also proposed at the testing stage to interpret the behaviors of the deep neural network. 5075 non-contrast chest CT scans were used as training, validation and testing datasets. Baseline performance was assessed on the same cohort. From the results, the proposed AID-Net achieved the superior performance on classification accuracy (0.9272) and AUC (0.9627).