Name
Abstract | ||
|---|---|---|
Deep learning has successfully been leveraged for medical image segmentation. It employs convolutional neural networks (CNN) to learn distinctive image features from a defined pixel-wise objective function. However, this approach can lead to less output pixel interdependence producing incomplete and unrealistic segmentation results. In this paper, we present a fully automatic deep learning method ... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TMI.2021.3060497 | IEEE Transactions on Medical Imaging |
Keywords | DocType | Volume |
Image segmentation,Feature extraction,Biomedical imaging,Shape,Decoding,Computed tomography,Feedback loop | Journal | 40 |
Issue | ISSN | Citations |
6 | 0278-0062 | 1 |
PageRank | References | Authors |
0.35 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kibrom Berihu Girum | 1 | 1 | 1.71 |
| Gilles Créhange | 2 | 7 | 1.00 |
| Alain Lalande | 3 | 101 | 15.31 |