• International Journal of Computer Science & Network Security
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• 제24권10호
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• pp.206-212
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• 2024

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.

• 전기의세계
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• 제26권4호
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• pp.54-60
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• 1977

The feedback sources of oculomotor control system consist of three types of feedback path originating from retinal image displacement, in the proprioceptive fibers of the extraocular muscles, in the efference copy within the C.N.S. From above feedback loops, the retinal image feedback path is a main subject in this experiment. The electrical output of eye ball motion detecting with a photo-electric matrix method is fed into galvanometer through the external feedback path, and the stability was also examined.

• 한국콘텐츠학회논문지
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• 제5권6호
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• pp.138-145
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• 2005

본 연구에서는 실명(loss of eyesight)의 원인 질병중 하나인 녹내장의 진행과 진단 등의 의료 정보제공을 목적으로 양방향 곡선 전개 방식을 이용하여 망막 영상에서 시신경 원판(optic disk)의 경계를 검출하는 방법을 제안한다. 정확한 경계 검출의 위하여 텍스처 병합(texture synthesis)기반의 이미지 인페인팅 방법으로 시신경 원판 위를 지나는 혈관을 제거하고 전처리 과정에서 발생하는 잡음제거와 경계의 보존을 위해 비등방성 확산 필터링(anisotropic diffusion filtering)을 행한다. 혈관이 제거된 망막 영상에서 시신경 원판의 경계 검출은 양방향 곡선 방식으로 검출한다. 실험 결과에서, 제안한 알고리즘은 복잡한 망막영상에도 효율적으로 시신경 원판을 검출한다는 것을 보여준다.