Earticle

초록

영어

In recent years, the deployment of deep neural networks in real-time clinical settings has been considered vulnerable due to domain shifts, which lowers their performance. Polyp image has significant appearance shifts, which eventually impact the performance. So, a deep learning model that generalizes unseen images is in high demand. This paper introduced a practical approach to improving domain shift issues. Firstly, we unified the style transfer with the segmentation model into one framework to diminish the appearance shifts problems and do segmentation alongside. Secondly, with the help of Adaptive Instance Normalization, we transferred the style precisely and dynamically in the earlier layers of the segmentation model. Our solution shows better results on the 224*224 image input than other baseline models.

목차

Abstract
I. INTRODUCTION
II. METHODOLOGY
III. DATASETS AND EXPERIMENTAL RESULTS
IV. CONCLUSION
REFERENCES

키워드

저자

• Astha Adhikari [ Department of AI Software Gachon University ]
• Sahadev Poudel [ Department of AI Software 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