Name
Abstract | ||
|---|---|---|
Quantification of anatomical shape changes currently relies on scalar global indexes which are largely insensitive to regional or asymmetric modifications. Accurate assessment of pathology-driven anatomical remodeling is a crucial step for the diagnosis and treatment of many conditions. Deep learning approaches have recently achieved wide success in the analysis of medical images, but they lack in... |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TMI.2020.2964499 | IEEE Transactions on Medical Imaging |
Keywords | DocType | Volume |
Shape,Pathology,Three-dimensional displays,Task analysis,Deep learning,Medical diagnostic imaging | Journal | 39 |
Issue | ISSN | Citations |
6 | 0278-0062 | 1 |
PageRank | References | Authors |
0.36 | 0 | 15 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Carlo Biffi | 1 | 27 | 4.97 |
| Georgia Doumou | 2 | 8 | 1.44 |
| Jinming Duan | 3 | 130 | 19.92 |
| Sanjay Prasad | 4 | 6 | 1.43 |
| Stuart A Cook | 5 | 111 | 8.45 |
| Declan P. O'Regan | 6 | 258 | 16.33 |
| Daniel Rueckert | 7 | 9338 | 637.58 |
| Juan J Cerrolaza | 8 | 115 | 17.01 |
| Giacomo Tarroni | 9 | 52 | 8.26 |
| Wenjia Bai | 10 | 445 | 35.84 |
| Antonio de Marvao | 11 | 60 | 4.27 |
| Ozan Oktay | 12 | 280 | 20.15 |
| Christian Ledig | 13 | 489 | 27.08 |
| Loïc Le Folgoc | 14 | 51 | 6.48 |
| Konstantinos Kamnitsas | 15 | 361 | 15.18 |