Name
Abstract | ||
|---|---|---|
Despite the potential to revolutionise disease diagnosis by performing data-driven classification, clinical interpretability of ConvNet remains challenging. In this paper, a novel clinical interpretable ConvNet architecture is proposed not only for accurate glaucoma diagnosis but also for the more transparent interpretation by highlighting the distinct regions recognised by the network. To the bes... |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/JBHI.2019.2949075 | IEEE Journal of Biomedical and Health Informatics |
Keywords | DocType | Volume |
Feature extraction,Semantics,Biomedical optical imaging,Optical imaging,Lesions,Convolution,Computer architecture | Journal | 24 |
Issue | ISSN | Citations |
5 | 2168-2194 | 4 |
PageRank | References | Authors |
0.39 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wangmin Liao | 1 | 4 | 0.39 |
| Beiji Zou | 2 | 231 | 41.61 |
| Rongchang Zhao | 3 | 9 | 3.81 |
| Yuanqiong Chen | 4 | 4 | 0.39 |
| Zhiyou He | 5 | 4 | 0.39 |
| Mengjie Zhou | 6 | 4 | 0.39 |