融合多维注意力机制CNN皮肤肿瘤图像分割提取 高正君.pdf

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计算机测量与通过对 ISIC2018 数据集及医院整形外科提供患者不同类型的
皮肤肿瘤图像进行分割测试，并将注意力模块随机组合形成的不同算法进行指标评价比对，所提出算法的平均分割精度可达
%;实验结果表明，所提出算法是有效可行的，在多维度下分割处理带复杂背景的皮肤病灶图像时有更高的鲁棒性。
关键词：皮肤肿瘤；图像分割；注意力模块；池化；卷积神经网络
中图法分类号 ；TP751 文献标识码 A

Combined Multidimensional Attention Mechanism Convolutional Neural
Network in Skin Tumor Image Segmentation
GAO Zhengjun1，ZHANG Peijiong2，SI Xiaoqiang1
( of Plastic Surgery, Gansu Provincial Hospital, Lanzhou 730030, China；
2. School of Water Conservancy and Electrical Engineering, Lanzhou Resource & Environment Voc-Tech University,
Lanzhou 730022,China）

Abstract: In medical image segmentation, convolutional neural network (CNN) is affected by the diversity
of skin lesions images, the location, shape and scale changes of segmentation targets, and other factors. A
multi-dimensional attention module based on space, channel and scale is proposed to optimize the convolutional
neural network image segmentation algorithm. Firstly, using the advantage of U-NET backbone network, its
purpose is to make image feature extraction more perfect. Second, composed of multidimensional space,
channels, scale attention mechanism identification of target lesion area detection, using the lower-level channel
cascade from the encoder image features and the decoder senior image adaptive weighting fusion of attention
unifies, enhance the awareness of the network on the sample lesions and discrimination, and highlight the most
relevant characteristics of the channel, Emphasize the most s