Earticle

초록

영어

In recent years, UNet architecture has shown to be a standard network for medical image segmentation. However, it suffers from some severe limitations. It loses localization ability for low-level details followed by the inability of long-range dependencies. Motivated by this, we explore transformer-based architectures that exploit global context by modeling long-range spatial dependencies, which are essential for accurate polyp segmentation. In this paper, we propose an attention-based transformer encoded UNet model. This hybrid model inherits both characteristics of CNN block as well as attention block. We perform various experiments in existing architectures like UNet, ResUNet, ResUNet-Mod and our proposed method. The proposed method achieved a 0.645 mIOU score took an unassailable lead over prior methods.

목차

Abstract
1. Introduction
2. Related Works
3. Methods
4. Experiments
4.1. Experimental setup
4.2. Experimental result
5. Conclusions
Acknowledgement
References

키워드

저자

• Raman Ghimire [ Department of IT Convergence Engineering Gachon University ]
• Sahadev Poudel [ Department of IT Convergence Engineering Gachon University ]
• Sang-Woong Lee [ Department of Software Gachon University ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004