To search, Click below search items. |
| All Published Papers Search Service |
|
|
|
Title |
Interpretability on Deep Retinal Image Understanding Network |
|
Author |
Manal AlGhamdi |
| Citation |
Vol. 24 No. 10 pp. 206-212 |
|
Abstract |
In the last 10 years, artificial intelligence (AI) has shown more predictive accuracy than humans in many fields. Its promising future founded on its great performance increases people’s concern about its black-box mechanism. In many fields, such as medicine, mistakes lacking explanations are hardly accepted. As a result, research on interpretable AI is of great significance. Although much work about interpretable AI methods are common in classification tasks, little has focused on segmentation tasks. In this paper, we explored the interpretability on a Deep Retinal Image Understanding (DRIU) network, which is used to segment the vessels from retinal images. We combine the Grad Class Activation Mapping (Grad-CAM), commonly used in image classification, to generate saliency map, with the segmentation task network. Through the saliency map, we got information about the contribution of each layer in the network during predicting the vessels. Therefore, we adjusted the weights of last convolutional layer manually to prove the accuracy of the saliency map generated by Grad-CAM. According to the result, we found the layer ‘upsample2’ to be the most important during segmentation, and we improved the mIoU score (an evaluation method) to some extent. |
|
Keywords |
Retinal image synthesis, generative adversarial networks, image-to-image translation, medical image segmentation. |
|
URL |
