Name
Abstract | ||
|---|---|---|
Nuclear segmentation is important and frequently demanded for pathology image analysis, yet is also challenging due to nuclear crowdedness and possible occlusion. In this paper, we present a novel bottomup method for nuclear segmentation. The concepts of Center Mask and Center Vector are introduced to better depict the relationship between pixels and nuclear instances. The instance differentiation process are thus largely simplified and easier to understand. Experiments demonstrate the effectiveness of Center Vector Encoding, where our method outperforms state-of-the-arts by a clear margin. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-20351-1_30 | INFORMATION PROCESSING IN MEDICAL IMAGING, IPMI 2019 |
Field | DocType | Volume |
Clear Margin,Pattern recognition,Computer science,Segmentation,Artificial intelligence,Pixel,Encoding (memory) | Conference | 11492 |
ISSN | Citations | PageRank |
0302-9743 | 2 | 0.35 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiahui Li | 1 | 6 | 1.45 |
| Zhiqiang Hu | 2 | 3 | 2.43 |
| yang shuang | 3 | 10 | 4.30 |