Name
Abstract | ||
|---|---|---|
Deep learning is largely applied to cell counting in microscopy images. However, most of the existing cell counting models are fully supervised and trained off-line. They adopt the usual training-testing framework, whereas the models are trained in advance to infer numbers of cells in test images. They require large amounts of manually labeled data for training but lack the ability to adapt to new... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICCVW54120.2021.00083 | 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) |
Keywords | DocType | Volume |
Training,Deep learning,Adaptation models,Microscopy,Manuals,Data structures,Real-time systems | Conference | 2021 |
Issue | ISSN | ISBN |
1 | 2473-9936 | 978-1-6654-0191-3 |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuang Zhu | 1 | 0 | 1.01 |
| Zhao Chen | 2 | 0 | 3.04 |
| Yuxin Zheng | 3 | 0 | 0.34 |
| Qinghua Zhang | 4 | 0 | 0.34 |
| Xuan Wang | 5 | 0 | 0.34 |