基于注意力机制的多尺度残差UNet实现乳腺癌灶分割

针对乳腺癌灶在磁共振成像（MRI）中呈现大小形状不一、边界模糊等特点，为避免误分割并提高分割精度，提出一种基于注意力机制的多尺度残差UNet分割算法。首先，利用多尺度残差单元替换UNet在下采样过程中的相邻两个卷积块以加强对形态大小差异的关注；接着，在上采样阶段使用跨层的注意力引导网络对重点区域的关注，避免造成对健康组织的误分割；最后，引入空洞空间金字塔池化作为分割网络的桥接模块以强化对病灶的表征能力。与UNet相比，所提算法在Dice系数、交并比（IoU）、特异度（SP）、准确度（ACC）等指标上分别提升了2.26、2.11、4.16、0.05个百分点。实验结果表明，所提算法能够提高癌灶分割精度，有效降低影像诊断的假阳性率。

Multiscale residual UNet based on attention mechanism to realize breast cancer lesion segmentation

Concerning the characteristics of breast cancer in Magnetic Resonance Imaging （MRI）， such as different shapes and sizes， and fuzzy boundaries， an algorithm based on multiscale residual U Network （UNet） with attention mechanism was proposed in order to avoid error segmentation and improve segmentation accuracy. Firstly， the multiscale residual units were used to replace two adjacent convolution blocks in the down-sampling process of UNet， so that the network could pay more attention to the difference of shape and size. Then， in the up-sampling stage， layer-crossed attention was used to guide the network to focus on the key regions， avoiding the error segmentation of healthy tissues. Finally， in order to enhance the ability of representing the lesions， the atrous spatial pyramid pooling was introduced as a bridging module to the network. Compared with UNet， the proposed algorithm improved the Dice coefficient， Intersection over Union （IoU）， SPecificity （SP） and ACCuracy （ACC） by 2.26， 2.11， 4.16 and 0.05 percentage points， respectively. The experimental results show that the algorithm can improve the segmentation accuracy of lesions and effectively reduce the false positive rate of imaging diagnosis.