Zhijian Xu
ABSTRACT
Automatic segmentation of skin lesions is of great significance for assisting doctors in early diagnosis. However, due to these problems such as color, size, boundary blur, low contrast, and hair occlusion, it is more difficult to automatically segment skin diseases. In order to overcome these challenges, reduce the missed diagnosis rate and misdiagnosis rate, prevent patients from missing the best treatment period, and improve patient survival. We proposed a new lightweight network based on multi-scale Transformer, which extracts rich global context dependencies through the multi-scale Transformer of four parallel paths, and adds a simple boundary enhancement structure to the last layer of the network as local information , and finally segmented by a layer-by-layer decoding structure. A large number of experiments were carried out on four public data sets. Through the analysis of objective evaluation indicators, our method has improved compared with other excellent methods. From the comparison of visual effects, we can also see that our method is more accurate in boundary segmentation. Our method improved the segmentation accuracy and reduces the scale of the model, which is conducive to the rapid automatic segmentation of skin diseases, assists skin doctors in diagnosis, and improves efficiency and accuracy.
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Index Terms
- Lightweight Multi-scale Transformer for Automatic Skin Lesions Segmentation
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