Reliable semi-supervised mutual learning framework for medical image segmentation-Reference-Cited by-同舟云学术

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Reliable semi-supervised mutual learning framework for medical image segmentation

Published:2025-01 Issue: Volume:99 Page:106798
ISSN:1746-8094
Container-title:Biomedical Signal Processing and Control
language:en
Short-container-title:Biomedical Signal Processing and Control

Author:

Hang Wenlong^ORCID,Bai Kui^ORCID,Liang Shuang^ORCID,Zhang Qingfeng,Wu Qiang,Jin Yukun^ORCID,Wang Qiong^ORCID,Qin Jing^ORCID

Funder

Natural Science Research of Jiangsu Higher Education Institutions of China

Publisher

Elsevier BV

Reference41 articles.

1. Attention-guided pyramid context network for polyp segmentation in colonoscopy images;Yue;IEEE Trans. Instrum. Meas.,2023

2. RECIST-induced reliable learning: Geometry-driven label propagation for universal lesion segmentation;Zhou;IEEE Trans. Med. Imaging,2023

3. D. Chen, Y. Bai, W. Shen, Q. Li, L. Yu, Y. Wang, Magicnet: Semi-supervised multi-organ segmentation via magic-cube partition and recovery, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 23869–23878.

4. Dynamic loss weighting for multiorgan segmentation in medical images;Song;IEEE Trans. Neural Netw. Learn. Syst.,2023

5. Uncertainty-aware self-ensembling model for semi-supervised 3D left atrium segmentation;Yu,2019

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