# Swin Transformer Assisted Prior Attention Network for Medical Image Segmentation
## 5 Conclusions  
In this paper, we proposed a novel network structure with the combination of two algorithms, called Swin-PANet, following the coarse-to-fine strategy and dual supervision strategy, and aimed to perform accurate segmentation of medical images including the challenging tasks of cell segmentation and skin lesion segmentation. The proposed Swin-PANet can be utilized for computer-aided diagnosis (CAD) of skin cancer to improve the segmentation efficiency and accuracy, considered as a significant technique for the accurate screening of diseased or abnormal area of patients to assist doctors to better evaluate disease and optimize prevention measures.  
In conclusion, the proposed Swin-PANet integrates both Swin Transformer and attention guiding decoder into prior attention network for performing intermediate learning and capturing global contextual information between pixel-level. Furthermore, an enhanced attention block is designed and proposed for utilizing feature fusion between global and local contexts along channel-wise, and better performing the coarse-to-fine strategy and enhancing attention ability of the network. Extensive experiments are conducted on three public datasets (GlaS, MoNuSeg, and ISIC 2016) for the overall evaluation of the proposed Swin-PANet. The quantitative and visual comparisons with state-of-the-art methods also demonstrate the effectiveness of the proposed Swin-PANet and excellent performance in the segmentation tasks based on our coarse-to-fine and dual supervision strategies.  
Although Swin-PANet achieves better performance than some state-of-the-art methods, the proposed network still has limitations in the ability of transfer learning. Recent work [2] demonstrates the Transformer has superior transferability for kinds of downstream tasks under pre-training. Our future work is to investigate the transferability of the combination of Swin Transformer and CNNs, and to design a more powerful and reliable network structure on medical image segmentation.  
The code and basic information can be downloaded at [[https://github.com/liaozhihao2757/Swin-PANet](https://github.com/liaozhihao2757/Swin-PANet)]([https://github.com/liaozhihao2757/Swin-PANet](https://github.com/liaozhihao2757/Swin-PANet)) (accessed on 27 December 2021).  
Conceptualization, Z.L., N.F. and K.X.; Data curation, Z.L.; Formal analysis, Z.L., N.F. and K.X.; Investigation, N.F.; Methodology, Z.L., N.F. and K.X.; Project administration, Z.L.; Resources, Z.L. and K.X.; Software, Z.L.; Supervision, Z.L., N.F. and K.X.; Validation, Z.L., N.F. and K.X.; Visualization, Z.L. and N.F.; Writing--original draft, Z.L., N.F. and K.X.; Writing--review and editing, Z.L., N.F. and K.X. These authors contributed equally to this work. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are publicly available at [[https://challenge.isic-archive.com/data/](https://challenge.isic-archive.com/data/)]([https://challenge.isic-archive.com/data/](https://challenge.isic-archive.com/data/)) (ISIC), [[https://monuseg.grand-challenge.org/Data/](https://monuseg.grand-challenge.org/Data/)]([https://monuseg.grand-challenge.org/Data/](https://monuseg.grand-challenge.org/Data/)) (MoNuSeg), and [[https://warwick.ac.uk/fac/cross_fac/tia/data/glascontest](https://warwick.ac.uk/fac/cross_fac/tia/data/glascontest)]([https://warwick.ac.uk/fac/cross_fac/tia/data/glascontest](https://warwick.ac.uk/fac/cross_fac/tia/data/glascontest)) (GlaS). The implemented code that supports the findings of this research are openly available at [[https://github.com/liaozhihao2757/Swin-PANet](https://github.com/liaozhihao2757/Swin-PANet)]([https://github.com/liaozhihao2757/Swin-PANet](https://github.com/liaozhihao2757/Swin-PANet)) (accessed on 27 December 2021).  
The authors declare no conflict of interest.