Name
Abstract | ||
|---|---|---|
•A framework for fundus image quality assessment filters the image with quality defects and provides visual feedback for real-time image reacquisition.•The proposed semi-tied adversarial discriminative domain adaptation model improves the generalization performance across different datasets with various distributions.•An efficient coarse-to-fine landmark detection (e.g. OD, fovea) is integrated into the architecture for robust quality assessment.•The DR grading task is improved with the proposed quality assessment preprocessing. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.media.2020.101654 | Medical Image Analysis |
Keywords | DocType | Volume |
Fundus image quality assessment,Domain adaptation,Interpretability,Multi-task learning | Journal | 61 |
ISSN | Citations | PageRank |
1361-8415 | 3 | 0.37 |
References | Authors | |
0 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yaxin Shen | 1 | 13 | 1.36 |
| Bin Sheng | 2 | 368 | 61.19 |
| Ruogu Fang | 3 | 287 | 21.78 |
| Huating Li | 4 | 22 | 5.14 |
| Ling Dai | 5 | 15 | 2.74 |
| Skylar Stolte | 6 | 3 | 0.37 |
| Jing Qin | 7 | 132 | 14.27 |
| Weiping Jia | 8 | 29 | 3.74 |
| Dinggang Shen | 9 | 7837 | 611.27 |