Earticle

초록

영어

Automatic skin lesion segmentation in terms of skin lesion analysis is very important. However, it is still a challenging task due to the irregular shapes of the skin lesion. Traditional CNN-based methods usually cannot achieve a satisfactory segmentation performance. We present a novel network with a feature enhanced Transformer for skin lesion segmentation. Unlike earlier CNN-based U-net models, our model utilizes Transformer blocks to capture global and local features, improving the performance of medical image segmentation. By incorporating feature enhancement module at every skip connection layer, we substantially enhance feature fusion capabilities and improve the efficiency of the encoderdecoder structure. In the FEM, a squeeze and excitation attention module is introduced to enhance important feature and suppress unnecessary information. The experimental results show that our proposed model demonstrated the effectiveness on PH2 dataset.

목차

Abstract
I. INTRODUCTION
II. PROBLEM FORMULATION
A. Definition
B. Problem
III. THE PROPOSED MODEL
A. Encoder
B. Decoder
C. Feature Enhancement Module
IV. EXPERIMENTAL RESULT
V. CONCLUSION
REFERENCES

키워드

저자

• Zhiguo Yan [ Department of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing, China ] Corresponding Author
• Lei Yan [ Department of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004