Name
Title | ||
|---|---|---|
How to Extract More Information with Less Burden: Fundus Image Classification and Retinal Disease Localization with Ophthalmologist Intervention |
Abstract | ||
|---|---|---|
Image classification using deep convolutional neural networks (DCNN) has a competitive performance as compare to other state-of-the-art methods. Here, attention can be visualized as a heatmap to improve the explainability of DCNN. We generated the initial heatmaps by using gradient-based classification activation map (Grad-CAM). We firstly assume that these Grad-CAM heatmaps can reveal the lesion regions well, then apply the attention mining on these heatmaps. Another, we assume that these Grad-CAM heatmaps can't reveal the lesion regions well then apply the dissimilarity loss on these Grad-CAM heatmaps. In this study, we asked the ophthalmologists to select 30% of the heatmaps. Furthermore, we design a knowledge preservation (KP) loss to minimize the discrepancy between heatmaps generated from the updated network and the selected heatmaps. Experiments revealed that our method improved accuracy from 90.1% to 96.2%. We also found that the attention regions are closer to the GT lesion regions. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ISBI45749.2020.9098600 | 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) |
Keywords | DocType | Volume |
Lesion localization,Grad-CAM,Attention mining,Knowledge preservation | Conference | PP |
Issue | ISSN | ISBN |
12 | 1945-7928 | 978-1-5386-9331-5 |
Citations | PageRank | References |
2 | 0.36 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qier Meng | 1 | 2 | 1.37 |
| Yohei Hashimoto | 2 | 2 | 0.36 |
| Shin'ichi Satoh | 3 | 7 | 3.83 |