[1]PENG Yutong,LIANG Fengmei.Tumor segmentation method for breast ultrasound images incorporating CNN and ViT[J].CAAI Transactions on Intelligent Systems,2024,19(3):556-564.[doi:10.11992/tis.202304046]
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Tumor segmentation method for breast ultrasound images incorporating CNN and ViT

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 3 Page number: 556-564 Column: 学术论文—机器学习 Public date: 2024-05-05
Title:
Tumor segmentation method for breast ultrasound images incorporating CNN and ViT
Author(s):
PENG Yutong;  LIANG Fengmei
College of Electronic Information and Optical Engineering, Taiyuan University of Technology, Jinzhong 030600, China
Keywords:
convolutional neural network; breast ultrasound image segmentation; Swin Transformer; crossover attention mechanism; hybrid-loss function; deformable convolution; multihead skip attention; deep learning
CLC:
TP391
DOI:
10.11992/tis.202304046
Abstract:
A segmentation method that fuses CNN and ViT is proposed to address the problems of large differences in shape and size of tumor regions of breast ultrasound images that lead to difficulty in segmentation, limitations in long-range dependency and spatial correlation in convolutional neural network (CNN) modeling, and the huge amount of data required by vision Transformer (ViT). Global and local detail features were extracted using a modified Swin Transformer module and a CNN encoder module based on deformable convolution, respectively. The design uses a cross-attention mechanism to fuse the feature representations of the two scales, and the training process adopts a binary cross-entropy loss combined with a boundary loss function. This approach effectively improves the segmentation accuracy. Experimental results on two public datasets show that the segmentation findings of the proposed method have been significantly improved compared with those of the existing classical algorithms, with a 3.8412% improvement in the dice coefficient. This outcome verifies the effectiveness and feasibility of the proposed method.
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