Name
Title | ||
|---|---|---|
Reducing the U-Net size for practical scenarios: Virus recognition in electron microscopy images |
Abstract | ||
|---|---|---|
•A light-weight U-Net for virus recognition in TEM images.•Reduction of over 23.2 million trainable weights without performance loss.•A comprehensive U-Net hyper-parameter selection strategy makes it lighter & faster.•Insights on the influence of U-Net hyper-parameter choices on performance. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.cmpb.2019.05.026 | Computer Methods and Programs in Biomedicine |
Keywords | DocType | Volume |
Deep learning,Hyper parameter optimization,Hardware integration,Transmission Electron Microscopy | Journal | 178 |
ISSN | Citations | PageRank |
0169-2607 | 2 | 0.40 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Damian J. Matuszewski | 1 | 5 | 1.49 |
| Ida-Maria Sintorn | 2 | 114 | 13.85 |