Mix Transformer depth-wise separable convolution UNet for breast mass segmentation in mammographic
Yutong Zhong, Yan Piao · 2023
Mammography is main tests used for breast cancer risk assessment. However, the mass segmentation and classification of mammograms are extremely challenging. To reduce computational costs and the workloads of radiologists, deep neural networks have been widely used in various medical image segmentation and classification tasks. Since there are some common features between these two tasks, a multi-task learning approach to solve both tasks are a promising direction. We propose a mix Transformer depth-wise separable convolution U-Network (MTDUNet) for mass segmentation of mammograms. We introduce depth-wise separable convolutions to replace traditional convolutions and improve the network’s perception of multi-scale features within the receptive field. Additionally, due to the inherent limitations of convolutional networks, we introduce mix transformer to model remote contextual information. We conducted evaluations the proposed GATNet on two publicly available breast mass segmentation datasets. The average Dice similarity coefficients between the MTDUNet results and INBreast and CBIS-DDSM data were 89.90% and 83.63%, respectively. The experimental results indicate that MTDUNet can significantly reduce the spatial complexity of medical image segmentation networks and effectively save computational resources.