Coronary calcium detection using 3D attention identical dual deep network based on weakly supervised learning
Open accessAbstract
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).
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Other studies that investigated deep learning algorithms for automated coronary calcium quantification used smaller cohorts or proprietary technologies.
“Although other studies have investigated deep learning algorithms for automated coronary calcium quantification34–42, they used smaller cohorts or proprietary technologies.”
From Deep convolutional neural networks to predict cardiovascular risk from computed tomography (2021) · cites this paper as [38]
The Abstract identifies this as a deep-learning study of coronary artery calcium detection using 5,075 CT scans. The Methods (Experiments) say its task was detecting the presence of calcium, not predicting a quantitative Agatston score. Our copy does not establish that its cohort was smaller than the citing study’s or that it used proprietary technology. (LLM, conf=0.87)
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- Authors
- Huo, Y., Terry, J. G., Wang, J., Nath, V., Bermudez, C., Bao, S., Parvathaneni, P., Carr, J. J., & Landman, B. A.
- Affiliations
- Vanderbilt University (US)
- ORCID
- Venue
- Medical Imaging 2019: Image Processing
- Publisher
- SPIE
- Published
- 2019-03-15
- Volume
- 10949
- Pages
- 42
- PMC
- PMC6874228
- OpenAlex
- W2899743512
- Cited by
- 22
- References
- 24
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 2 · 2023: 1 · 2022: 4 · 2021: 5 · 2020: 4 · 2019: 1
- FWCI
- 5.30
- Type
- conference paper
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- SPIE Medical Imaging: Image Processing (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1R01EB017230, 1452485, ULTR000445, CAREER, UL1 TR000445, UL1 RR024975-01, 2 UL1 TR000445-06)
- Patient-Centered Outcomes Research Institute (CDRN-1306-04869)
- Vanderbilt University (UL1 RR024975-01, TR000445, UL1 RR024975, UL1‐TR000445, VR3029, ULTR000445, ViSE/VICTR VR3029)
- Nvidia
- Vanderbilt Institute for Clinical and Translational Research (VR3029, UL1 TR000445, RR024975, 2 UL1 TR000445-06, ULTR000445, UL1 RR024975-01, UL1 RR024975)
- Vanderbilt University Medical Center (2 UL1 TR000445-06, UL1 RR024975, ULTR000445, UL1 RR024975-01, VR3029)
- Georgia Clinical and Translational Science Alliance (UL1 RR024975, UL1 RR024975-01, RR024975, TR000445, ULTR000445)
- National Institutes of Health (VR3029, ViSE/VICTR VR3029, UL1TR000445, TR000445, RR024975, ULTR000445)
- National Eye Institute (R21EY024036)
- National Institute of Neurological Disorders and Stroke (R21NS064534)
- National Institute of Biomedical Imaging and Bioengineering (R03EB012461, R01EB017230)
- National Center for Research Resources (RR024975, TR000445, UL1RR024975, RR024975‐01)
- National Center for Advancing Translational Sciences (TR-000445, TR000445-06, UL‐TR‐000445)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Cardiac Imaging and Diagnostics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- coronary artery disease, weakly supervised learning, non-contrast chest CT, Grad-CAM, subclinical atherosclerosis, myocardial infarction risk
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