基于U型稠密特征融合的皮肤病灶分割

皮肤病灶图像分割可作为医学相关类疾病辅助诊断的重要依据。针对皮肤病灶区域结构复杂和尺度信息参差错落的特点，提出一种基于U型稠密特征融合的皮肤病灶分割方法。编码器利用稠密网络结构和空洞空间金字塔池化充分提取特征与融合，由稠密空间注意力模块与深度可分离卷积解码深层特征，防止病灶区域周围噪声干扰，同时引入融合压缩注意力模块进一步提高分割性能，通过二值交叉熵与Jaccard系数结合的损失函数优化。在ISBI 2016皮肤病灶数据集进行仿真评估，Jaccard相似度和Dice系数分别达到86.87%和92.98%，有助于提高皮肤病灶诊断效率。

Segmentation of skin lesions based on U-shaped dense feature fusion

Abstract: The image segmentation of skin lesions can be used as an important basis for the auxiliary diagnosis of related diseases. Considering the complex structure and uneven scale information of skin lesions, a novel skin lesion segmentation method based on U-shaped dense feature fusion is proposed. The dense network structure and atrous spatial pyramid pooling are adopted in encoder for feature extraction and fusion. The dense spatial attention module and the depthwise separable convolution are used to decode deep features to prevent noise interference around the focal area. Moreover, the segmentation performance is further improved by blend squeeze attention module, and the proposed algorithm is optimized by loss function combining binary cross entropy and Jaccard coefficient. The Jaccard similarity and Dice coefficient in the simulation evaluation on ISBI 2016 skin lesions datasets were 86.87% and 92.98%, respectively. The proposed method is conducive to improving the diagnosis efficiency of skin lesions.