Gated axial transformer network for mass segmentation in mammographic
Yutong Zhong, Yan Piao · 2023
Mammography is main tests used for breast cancer risk assessment. However, due to the high similarity of the mass to breast tissue, blurring of the mass edges causes the task of segmenting the breast mass on mammograms to be challenging. To reduce computational costs and the workloads of radiologists, deep learning techniques based on computer vision have become a common implementation in the field of medical image segmentation. However, owing to the locality of the convolutional operation, the neural networks cannot effectively learn global and remote semantic information. We propose a novel gated axial transformer network (GATNet) framework for the mass segmentation of mammograms. GATNet uses an encoder-decoder structure. First, we use axial attention to decompose 2D self-attention into two one-dimensional self attentions. Second, we construct an efficient location-sensitive gated transformer module for image features to establish remote contextual dependencies. We conducted evaluations the proposed GATNet on two publicly available breast mass segmentation datasets. The average Dice similarity coefficients between the GATNet results and INBreast and CBISDDSM data were 80.98% and 83.63%, respectively. The experimental results indicate that GATNet effectively reduces both the computational and spatial complexity of the medical image segmentation network, demonstrating its remarkable performance.