  Incorporating various mass shapes and sizes in training deep learning
architectures has made breast mass segmentation challenging. Moreover, manual
segmentation of masses of irregular shapes is time-consuming and error-prone.
Though Deep Neural Network has shown outstanding performance in breast mass
segmentation, it fails in segmenting micro-masses. In this paper, we propose a
novel U-net-shaped transformer-based architecture, called Swin-SFTNet, that
outperforms state-of-the-art architectures in breast mammography-based
micro-mass segmentation. Firstly to capture the global context, we designed a
novel Spatial Feature Expansion and Aggregation Block(SFEA) that transforms
sequential linear patches into a structured spatial feature. Next, we combine
it with the local linear features extracted by the swin transformer block to
improve overall accuracy. We also incorporate a novel embedding loss that
calculates similarities between linear feature embeddings of the encoder and
decoder blocks. With this approach, we achieve higher segmentation dice over
the state-of-the-art by 3.10% on CBIS-DDSM, 3.81% on InBreast, and 3.13% on
CBIS pre-trained model on the InBreast test data set.
