Name
Abstract | ||
|---|---|---|
Uncertain data classification makes it possible to reduce the decision risk through abstaining from classifying uncertain cases. Incorporating this idea into the process of computer aided diagnosis can greatly reduce the risk of misdiagnosis. However, for deep neural networks, most existing models lack a strategy to handle uncertain data and thus suffer the costs of serious classification errors. To tackle this problem, we utilize Dempster-Shafer evidence theory to measure the uncertainty of the prediction output by deep neural networks and thereby propose an uncertain data classification method with evidential deep neural networks (EviNet-UC). The proposed method can effectively improve the recall rate of the risky class through involving the evidence adjustment in the learning objective. Experiments on medical images show that the proposed method is effective to identify uncertain data instances and reduce the decision risk. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/978-3-030-55393-7_38 | KSEM (2) |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bin Yuan | 1 | 10 | 6.32 |
| Xiaodong Yue | 2 | 248 | 21.94 |
| Ying Lv | 3 | 0 | 1.69 |
| Thierry Denoeux | 4 | 815 | 74.98 |