Name
Abstract | ||
|---|---|---|
Methods based on convolutional neural networks have improved the performance of biomedical image segmentation. However, most of these methods cannot efficiently segment objects of variable sizes and train on small and biased datasets, which are common for biomedical use cases. While methods exist that incorporate multi-scale fusion approaches to address the challenges arising with variable sizes, ... |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/JBHI.2021.3138024 | IEEE Journal of Biomedical and Health Informatics |
Keywords | DocType | Volume |
Image segmentation,Computer architecture,Feature extraction,Decoding,Shape,Semantics,Annotations | Journal | 26 |
Issue | ISSN | Citations |
5 | 2168-2194 | 1 |
PageRank | References | Authors |
0.43 | 0 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abhishek Srivastava | 1 | 1 | 0.43 |
| Debesh Jha | 2 | 11 | 5.79 |
| Sukalpa Chanda | 3 | 1 | 0.43 |
| Umapada Pal | 4 | 1477 | 139.32 |
| Dag Johansen | 5 | 548 | 64.93 |
| Dag Johansen | 6 | 548 | 64.93 |
| Michael Riegler | 7 | 9 | 12.50 |
| Sharib Ali | 8 | 4 | 2.52 |
| Pal Halvorsen | 9 | 173 | 30.93 |