MS-UNet: Multi-Scale Nested UNet for Medical Image Segmentation with Few Training Data Based on an ELoss and Adaptive Denoising Method

Haoyuan Chen, Yufei Han, Linwei Yao, Xin Wu, Kuan Li () and Jianping Yin
Additional contact information
Haoyuan Chen: School of Computer Science and Technology, Dongguan University of Technology, Dongguan 523808, China
Yufei Han: School of Computer Science and Technology, Dongguan University of Technology, Dongguan 523808, China
Linwei Yao: School of Computer Science and Technology, Dongguan University of Technology, Dongguan 523808, China
Xin Wu: School of Computer Science and Technology, Dongguan University of Technology, Dongguan 523808, China
Kuan Li: School of Computer Science and Technology, Dongguan University of Technology, Dongguan 523808, China
Jianping Yin: School of Computer Science and Technology, Dongguan University of Technology, Dongguan 523808, China

Mathematics, 2024, vol. 12, issue 19, 1-19

Abstract: Traditional U-shape segmentation models can achieve excellent performance with an elegant structure. However, the single-layer decoder structure of U-Net or SwinUnet is too “thin” to exploit enough information, resulting in large semantic differences between the encoder and decoder parts. Things get worse in the field of medical image processing, where annotated data are more difficult to obtain than other tasks. Based on this observation, we propose a U-like model named MS-UNet with a plug-and-play adaptive denoising module and ELoss for the medical image segmentation task in this study. Instead of the single-layer U-Net decoder structure used in Swin-UNet and TransUNet, we specifically designed a multi-scale nested decoder based on the Swin Transformer for U-Net. The proposed multi-scale nested decoder structure allows for the feature mapping between the decoder and encoder to be semantically closer, thus enabling the network to learn more detailed features. In addition, ELoss could improve the attention of the model to the segmentation edges, and the plug-and-play adaptive denoising module could prevent the model from learning the wrong features without losing detailed information. The experimental results show that MS-UNet could effectively improve network performance with more efficient feature learning capability and exhibit more advanced performance, especially in the extreme case with a small amount of training data. Furthermore, the proposed ELoss and denoising module not only significantly enhance the segmentation performance of MS-UNet but can also be applied individually to other models.

Keywords: medical image segmentation; Swin Transformer; multi-scale nested decoder; ELoss; plug-and-play adaptive denoising module (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/19/2996/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/19/2996/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:19:p:2996-:d:1486206

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().