Name
Title | ||
|---|---|---|
Segmentation and quantification of infarction without contrast agents via spatiotemporal generative adversarial learning. |
Abstract | ||
|---|---|---|
•Simultaneous segmentation and quantification of infarction without contrast agent.•Learning time-series images by using spatiotemporal pyramid representation.•Improving performance by exploiting the commonalities and differences across tasks.•Embedding segmentation and quantification tasks into adversarial learning. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.media.2019.101568 | Medical Image Analysis |
Keywords | Field | DocType |
Myocardial infarction,Segmentation,Full quantification,Sequential images,Generative adversarial networks | Computer vision,ENCODE,Surgical planning,Kinematics,Pattern recognition,Segmentation,Abnormality,Artificial intelligence,Encoder,Centroid,Mathematics,Generative model | Journal |
Volume | ISSN | Citations |
59 | 1361-8415 | 2 |
PageRank | References | Authors |
0.38 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chenchu Xu | 1 | 5 | 2.10 |
| Joanne Howey | 2 | 4 | 0.73 |
| Pavlo Ohorodnyk | 3 | 7 | 1.10 |
| Mike Roth | 4 | 2 | 0.38 |
| Heye Zhang | 5 | 397 | 52.64 |
| Shuo Li | 6 | 109 | 27.59 |