Name
Title | ||
|---|---|---|
Clinical Report Guided Retinal Microaneurysm Detection With Multi-Sieving Deep Learning. |
Abstract | ||
|---|---|---|
Timely detection and treatment of microaneurysms is a critical step to prevent the development of vision-threatening eye diseases such as diabetic retinopathy. However, detecting microaneurysms in fundus images is a highly challenging task due to the low image contrast, misleading cues of other red lesions, and the large variation of imaging conditions. Existing methods tend to fail in face of the... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TMI.2018.2794988 | IEEE Transactions on Medical Imaging |
Keywords | Field | DocType |
Image segmentation,Lesions,Hemorrhaging,Semantics,Retinopathy,Image color analysis,Retina | Computer vision,Feature vector,Pattern recognition,Convolutional neural network,Feature (computer vision),Semantic gap,Fundus (eye),Image segmentation,Artificial intelligence,Deep learning,Microaneurysm,Mathematics | Journal |
Volume | Issue | ISSN |
37 | 5 | 0278-0062 |
Citations | PageRank | References |
7 | 0.58 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ling Dai | 1 | 9 | 1.69 |
| Ruogu Fang | 2 | 287 | 21.78 |
| Huating Li | 3 | 26 | 2.01 |
| Xuhong Hou | 4 | 47 | 4.03 |
| Bin Sheng | 5 | 368 | 61.19 |
| Qiang Wu | 6 | 7 | 0.58 |
| Weiping Jia | 7 | 29 | 3.74 |