Name
Title | ||
|---|---|---|
Epithelium-Stroma Classification via Convolutional Neural Networks and Unsupervised Domain Adaptation in Histopathological Images. |
Abstract | ||
|---|---|---|
Epithelium-stroma classification is a necessary preprocessing step in histopathological image analysis. Current deep learning based recognition methods for histology data require collection of large volumes of labeled data in order to train a new neural network when there are changes to the image acquisition procedure. However, it is extremely expensive for pathologists to manually label sufficien... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/JBHI.2017.2691738 | IEEE Journal of Biomedical and Health Informatics |
Keywords | Field | DocType |
Neural networks,Training,Kernel,Feature extraction,Image analysis,Machine learning,Adaptation models | Semi-supervised learning,Computer science,Convolutional neural network,Transfer of learning,Artificial intelligence,Deep learning,Artificial neural network,Computer vision,Pattern recognition,Feature extraction,Preprocessor,Test data,Machine learning | Journal |
Volume | Issue | ISSN |
21 | 6 | 2168-2194 |
Citations | PageRank | References |
9 | 0.53 | 17 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yue Huang | 1 | 317 | 29.82 |
| Han Zheng | 2 | 11 | 2.27 |
| Liu, C. | 3 | 10 | 1.28 |
| Xinghao Ding | 4 | 591 | 52.95 |
| Gustavo K. Rohde | 5 | 395 | 41.81 |