Name
Title | ||
|---|---|---|
Exploiting the potential of unlabeled endoscopic video data with self-supervised learning. |
Abstract | ||
|---|---|---|
As it makes efficient use of available (non-)public and (un-)labeled data, the approach has the potential to become a valuable tool for CNN (pre-)training. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s11548-018-1772-0 | Int. J. Computer Assisted Radiology and Surgery |
Keywords | DocType | Volume |
Self-supervised learning,Endoscopic instrument segmentation,Transfer learning,Endoscopic image processing,Computer vision | Journal | abs/1711.09726 |
Issue | ISSN | Citations |
6 | 1861-6410 | 4 |
PageRank | References | Authors |
0.39 | 14 | 13 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tobias Roß | 1 | 4 | 0.39 |
| David Zimmerer | 2 | 4 | 0.39 |
| Anant Suraj Vemuri | 3 | 14 | 3.67 |
| Fabian Isensee | 4 | 78 | 4.87 |
| Sebastian Bodenstedt | 5 | 91 | 16.46 |
| Fabian Both | 6 | 21 | 2.04 |
| Philip Kessler | 7 | 4 | 0.39 |
| Martin Wagner | 8 | 75 | 15.76 |
| Beat P. Müller-Stich | 9 | 79 | 12.09 |
| Hannes Kenngott | 10 | 104 | 22.28 |
| Stefanie Speidel | 11 | 313 | 39.70 |
| Klaus H. Maier-Hein | 12 | 361 | 42.06 |
| Lena Maier-Hein | 13 | 626 | 80.20 |