SWIN-SFTNet : Spatial Feature Expansion and Aggregation using Swin Transformer For Whole Breast micro-mass segmentation. (arXiv:2211.08717v1 [eess.IV])

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 …

aggregation arxiv expansion feature segmentation swin transformer