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Coronary calcium detection using 3D attention identical dual deep network based on weakly supervised learning
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Paper 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).

Paper Details

Date Published: 15 March 2019
PDF: 8 pages
Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 1094917 (15 March 2019); doi: 10.1117/12.2512541
Show Author Affiliations
Yuankai Huo, Vanderbilt Univ. (United States)
James G. Terry, Vanderbilt Univ. (United States)
Jiachen Wang, Vanderbilt Univ. (United States)
Vishwesh Nath, Vanderbilt Univ. (United States)
Camilo Bermudez, Vanderbilt Univ. (United States)
Shunxing Bao, Vanderbilt Univ. (United States)
Prasanna Parvathaneni, Vanderbilt Univ. (United States)
J. Jeffery Carr, Vanderbilt Univ. (United States)
Bennett A. Landman, Vanderbilt Univ. (United States)


Published in SPIE Proceedings Vol. 10949:
Medical Imaging 2019: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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