Dead space 2 peng wallpaper9/16/2023 ![]() We evaluated the segmentation performance of the proposed CARNet on the IDRiD, E-ophtha and DDR data sets. ![]() The high-level refinement decoder uses dual attention mechanism to integrate the same-level features in the two encoders with the output of the low-level attention refinement module for multiscale information fusion, which focus the model on the lesion area to generate accurate predictions. We take the whole image and the patch image as the dual input, and feed them to ResNet50 and ResNet101, respectively, for downsampling to extract lesion features. CARNet is composed of global image encoder, local image encoder and attention refinement decoder. It can make full use of the fine local details and coarse global information from the fundus image. To address the issue, this paper proposes a cascade attentive RefineNet (CARNet) for automatic and accurate multi-lesion segmentation of diabetic retinopathy. However, the task of lesion segmentation is full of challenges due to the complex structure, the various sizes and the interclass similarity with other fundus tissues. Lesion segmentation from fundus images helps ophthalmologists accurately diagnose and grade of diabetic retinopathy. ![]() Diabetic retinopathy is the leading cause of blindness in working population. ![]()
0 Comments
Leave a Reply.AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |