ArticleProceedings of SPIE--the International Society for Optical Engineering2019
Coronary Calcium Detection using 3D Attention Identical Dual Deep Network Based on Weakly Supervised Learning.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 20 citations in OpenAlex.
- Explainable Artificial Intelligence in Radiological Cardiovascular Imaging-A Systematic Review.Diagnostics (Basel, Switzerland) · 2025Review
- A Survey on Explainable Artificial Intelligence (XAI) Techniques for Visualizing Deep Learning Models in Medical Imaging.Journal of imaging · 2024Article
- Deep learning applications in coronary anatomy imaging: a systematic review and meta-analysis.Journal of medical artificial intelligence · 2022Article
- Using artificial intelligence in the development of diagnostic models of coronary artery disease with imaging markers: A scoping review.Frontiers in cardiovascular medicine · 2022Article
- Semantic Cardiac Segmentation in Chest CT Images Using K-Means Clustering and the Mathematical Morphology Method.Sensors (Basel, Switzerland) · 2021Article
- Deep convolutional neural networks to predict cardiovascular risk from computed tomography.Nature communications · 2021Article
- Molecular Image-Based Prediction Models of Nuclear Receptor Agonists and Antagonists Using the DeepSnap-Deep Learning Approach with the Tox21 10K Library.Molecules (Basel, Switzerland) · 2020Article
- Semi-supervised Machine Learning with MixMatch and Equivalence Classes.Lecture notes-monograph series · 2020Article
- Anatomical context improves deep learning on the brain age estimation task.Magnetic resonance imaging · 2019Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
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).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.