Earticle

초록

영어

Accurate segmentation of breast tumors is essential for early cancer diagnosis, particularly in young women. Ultrasound imaging provides a non-invasive and cost-effective screening tool, yet breast ultrasound (BUS) segmentation is challenging due to tumor variability, blurred boundaries, and limited annotated datasets. To address annotation scarcity, we propose a semi-supervised framework that leverages both labeled and unlabeled BUS images. Pseudo-labels are generated for unlabeled data and refined with a multi-view strategy and self-consistency loss to improve reliability and stability. On the BUSI dataset, our method achieved Dice scores of 81%, 78%, and 76% with 1/2, 1/4, and 1/8 labeled data, respectively. We find these results to be equal to or superior to state-of-the-art approaches. Through this work, we can significantly reduce the annotation demands and expert labor required from radiologists.

목차

Abstract
1. INTRODUCTION
2. THEORY
2.1 Pseudo-labeling with different views
2.2 Self consistency based re-training
3. EXPERIMENTS
4. RESULTS AND DISCUSSION
5. CONCLUSION
References

키워드

저자

• Seokmin Han [ Professor, Major of Data Science, Korea National University of Transportation Uiwang-si, Gyunggi-do, South Korea. ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Internet, Broadcasting and Communication

• 간기

  계간

• pISSN

  2288-4920

• eISSN

  2288-4939

• 수록기간

  2009~2025

• 십진분류

  KDC 326 DDC 380