RMS-SE-UNet: A Segmentation Method for Tumors in Breast Ultrasound Images

It is a significant challenge to obtain accurate boundary of tumors due to much speckle noises. In this paper, we proposed some meaningful modules to address the problem. Firstly, a large amount of semantic information is needed to determine the boundary on account of fuzziness around the edge of breast tumor, we proposed residual multi-scale(RMS) block to collect larger receptive filed. Secondly, we redesigned the skip connections and combined Squeeze-and-Excitation(SE) block to incorporate information from different layers. Finally we introduced deep supervision and hybrid loss function to accelerate the convergence of network. Dice similarity coefficient and intersection over union(IoU) were used to evaluate segmentation results which were 94.69 % and 90.01 % respectively on test set. It is shown that our method is effective in this kind of problem.