Name
Abstract | ||
|---|---|---|
This paper presents a deep random walk technique for drusen segmentation from fundus images. It is formulated as a deep learning architecture which learns deep representations from fundus images and specify an optimal pixel-pixel affinity. Specifically, the proposed architecture is mainly composed of three parts: a deep feature extraction module to learn both semantic-level and low-level representation of image, an affinity learning module to get pixel-pixel affinities for formulating the transition matrix of random walk and a random walk module which propagates manual labels. The power of our technique comes from the fact that the learning procedures for deep image representations and pixel-pixel affinities are driven by the random walk process. The accuracy of our proposed algorithm surpasses state-of-the-art drusen segmentation techniques as validated on the public STARE and DRIVE databases. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-00934-2_6 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Drusen segmentation,Retinal fundus images,Deep feature extraction,Affinity learning,Random walk | Computer vision,Pattern recognition,Stochastic matrix,Computer science,Random walk,Segmentation,Drusen,Fundus (eye),Feature extraction,Artificial intelligence,Deep learning | Conference |
Volume | ISSN | Citations |
11071 | 0302-9743 | 1 |
PageRank | References | Authors |
0.34 | 6 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fang Yan | 1 | 8 | 3.23 |
| Jia Cui | 2 | 1 | 1.02 |
| Yu Wang | 3 | 167 | 28.47 |
| Hong Liu | 4 | 139 | 22.83 |
| Hui Liu | 5 | 4 | 1.79 |
| Benzheng Wei | 6 | 85 | 7.52 |
| Yilong Yin | 7 | 966 | 135.80 |
| Yuanjie Zheng | 8 | 671 | 55.01 |