Name
Title | ||
|---|---|---|
Context-constrained multiple instance learning for histopathology image segmentation. |
Abstract | ||
|---|---|---|
Histopathology image segmentation plays a very important role in cancer diagnosis and therapeutic treatment. Existing supervised approaches for image segmentation require a large amount of high quality manual delineations (on pixels), which is often hard to obtain. In this paper, we propose a new algorithm along the line of weakly supervised learning; we introduce context constraints as a prior for multiple instance learning (ccMIL), which significantly reduces the ambiguity in weak supervision (a 20% gain); our method utilizes image-level labels to learn an integrated model to perform histopathology cancer image segmentation, clustering, and classification. Experimental results on colon histopathology images demonstrate the great advantages of ccMIL. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-33454-2_77 | Lecture Notes in Computer Science |
Field | DocType | Volume |
Computer vision,Scale-space segmentation,Pattern recognition,Computer science,Local binary patterns,Supervised learning,Image segmentation,Pixel,Artificial intelligence,Cluster analysis,Ambiguity | Conference | 7512 |
Issue | ISSN | Citations |
Pt 3 | 0302-9743 | 15 |
PageRank | References | Authors |
0.65 | 10 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yan Xu | 1 | 243 | 16.75 |
| Jianwen Zhang | 2 | 319 | 14.74 |
| Eric I-Chao Chang | 3 | 261 | 12.68 |
| Maode Lai | 4 | 121 | 4.81 |
| Zhuowen Tu | 5 | 3663 | 215.79 |