Name
Abstract | ||
|---|---|---|
Robust cell detection in histopathological images is a crucial step in the computer-assisted diagnosis methods. In addition, recent studies show that subtypes play an significant role in better characterization of tumor growth and outcome prediction. In this paper, we propose a novel subtype cell detection method with an accelerated deep convolution neural network. The proposed method not only detects cells but also gives subtype cell classification for the detected cells. Based on the subtype cell detection results, we extract subtype cell related features and use them in survival prediction. We demonstrate that our proposed method has excellent subtype cell detection performance and our proposed subtype cell features can achieve more accurate survival prediction. |
Year | Venue | Field |
|---|---|---|
2016 | MICCAI | Pattern recognition,Computer science,Convolutional neural network,Speech recognition,Cell,Artificial intelligence |
DocType | Citations | PageRank |
Conference | 9 | 0.57 |
References | Authors | |
11 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sheng Wang | 1 | 56 | 7.12 |
| Jiawen Yao | 2 | 168 | 13.10 |
| Zheng Xu | 3 | 31 | 7.18 |
| Junzhou Huang | 4 | 2182 | 141.43 |