Name
Title | ||
|---|---|---|
Coronary Calcium Detection using 3D Attention Identical Dual Deep Network Based on Weakly Supervised Learning. |
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). |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1117/12.2512541 | Proceedings of SPIE |
Keywords | DocType | Volume |
3D grad-cam,attention,CAC,coronary artery calcium,AID-Net | Conference | 10949 |
ISSN | Citations | PageRank |
0277-786X | 0 | 0.34 |
References | Authors | |
0 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuankai Huo | 1 | 96 | 26.45 |
| James G. Terry | 2 | 1 | 1.04 |
| Jiachen Wang | 3 | 1 | 1.38 |
| Vishwesh Nath | 4 | 24 | 5.95 |
| Camilo Bermudez | 5 | 32 | 3.09 |
| Shunxing Bao | 6 | 46 | 8.53 |
| Prasanna Parvathaneni | 7 | 40 | 6.94 |
| J. Jeffrey Carr | 8 | 0 | 0.34 |
| Bennett A. Landman | 9 | 700 | 74.20 |