Name
Abstract | ||
|---|---|---|
This paper presents and evaluates a fully automatic method for detection of ductal carcinoma in situ (DCIS) in digitized hematoxylin and eosin (H&E) stained histopathological slides of breast tissue. The proposed method applies multi-scale superpixel classification to detect epithelial regions in whole-slide images (WSIs). Subsequently, spatial clustering is utilized to delineate regions represent... |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TMI.2016.2550620 | IEEE Transactions on Medical Imaging |
Keywords | Field | DocType |
Lesions,Cancer,Feature extraction,Clustering algorithms,Pathology,Design automation,Breast tissue | H&E stain,Computer vision,Ductal carcinoma,Pattern recognition,Histopathology,Artificial intelligence,Medicine,Pathology,False positive paradox | Journal |
Volume | Issue | ISSN |
35 | 9 | 0278-0062 |
Citations | PageRank | References |
13 | 0.78 | 16 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Babak Ehteshami Bejnordi | 1 | 720 | 30.27 |
| Maschenka Balkenhol | 2 | 31 | 2.24 |
| Geert Litjens | 3 | 996 | 50.79 |
| Roland Holland | 4 | 13 | 0.78 |
| Peter Bult | 5 | 14 | 1.46 |
| Nico Karssemeijer | 6 | 992 | 122.49 |
| j a w m van der laak | 7 | 702 | 29.53 |