Name
Abstract | ||
|---|---|---|
Anomaly detection in medical images refers to the identification of abnormal images with only normal images in the training set. Most existing methods solve this problem with a self-reconstruction framework, which tends to learn an identity mapping and reduces the sensitivity to anomalies. To mitigate this problem, in this paper, we propose a novel Proxy-bridged Image Reconstruction Network (Proxy... |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TMI.2021.3118223 | IEEE Transactions on Medical Imaging |
Keywords | DocType | Volume |
Image reconstruction,Anomaly detection,Biomedical imaging,Retina,Feature extraction,Magnetic resonance imaging,Training | Journal | 41 |
Issue | ISSN | Citations |
3 | 0278-0062 | 0 |
PageRank | References | Authors |
0.34 | 27 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kang Zhou | 1 | 61 | 4.87 |
| Jing Li | 2 | 0 | 0.34 |
| Weixin Luo | 3 | 92 | 8.23 |
| Zhengxin Li | 4 | 4 | 2.41 |
| Jianlong Yang | 5 | 18 | 4.01 |
| Huazhu Fu | 6 | 1235 | 65.07 |
| Jun Cheng | 7 | 214 | 20.65 |
| Jiang Liu | 8 | 299 | 42.50 |
| Shenghua Gao | 9 | 1607 | 66.89 |