Name
Title | ||
|---|---|---|
A Weakly-Supervised Anomaly Detection Method via Adversarial Training for Medical Images |
Abstract | ||
|---|---|---|
Convolutional neural networks have been widely used for anomaly detection and one of their most common methods is autoencoder. The autoencoder is expected to produce lower reconstruction error for the normal data than the abnormal ones, and the reconstruction error is typically set as a measurement index for distinguishing anomalies. In practice, however, this notion is not always compatible. The ... |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/ICCE53296.2022.9730129 | 2022 IEEE International Conference on Consumer Electronics (ICCE) |
Keywords | DocType | ISBN |
Learning systems,Training,Measurement uncertainty,Liver,Generators,Indexes,Image reconstruction | Conference | 978-1-6654-4154-4 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| He Li | 1 | 0 | 0.34 |
| Yutaro Iwamoto | 2 | 0 | 0.34 |
| Xianhua Han | 3 | 0 | 0.34 |
| Lanfen Lin | 4 | 78 | 24.70 |
| Ruofeng Tong | 5 | 466 | 49.69 |
| Hongjie Hu | 6 | 11 | 9.50 |
| Akira Furukawa | 7 | 0 | 0.34 |
| Shuzo Kanasaki | 8 | 0 | 0.34 |
| Yen-Wei Chen | 9 | 720 | 155.73 |