Melanoma image segmentation method based on edge key points and edge attention
High-precision segmentation of melanoma images is crucial for early diagnosis and improving patient survival.However,the blurring of the edge region of melanoma,which presents irregular shapes,makes it difficult for existing segmentation methods to obtain edge feature information,affecting the accuracy of melanoma image segmentation.To solve this problem,a melanoma image segmentation method based on edge key points and edge attention is proposed.An edge key point selection module for point rendering and a combined convolution transformer block are designed in the encoder to guide the acquisition of local and global features of the edge by selecting edge key points.Then,the edge refinement module is designed in the encoder to refine the edge features of the deep network,and finally,the multi-scale edge attention module is designed in the skip connection,which enables the capture of the edge shape features at multiple scales.The tests on two datasets(ISIC 2018 and PH2)demonstrate that the proposed method has better segmentation performance than the existing segmentation methods.
melanomaimage segmentationmulti-scaleedge attentionedge key point selection
万方数据
维普
