Semi-supervised image segmentation using a residual-driven mean teacher and an exponential Dice loss-Reference-Cited by-同舟云学术

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Semi-supervised image segmentation using a residual-driven mean teacher and an exponential Dice loss

Published:2024-02 Issue: Volume:148 Page:102757
ISSN:0933-3657
Container-title:Artificial Intelligence in Medicine
language:en
Short-container-title:Artificial Intelligence in Medicine

Author:

Mei Chenyang,Yang Xiaoguo,Zhou Mi,Zhang Shaodan,Chen Hao,Yang Xiaokai,Wang Lei

Funder

National Natural Science Foundation of China

Natural Science Foundation of Zhejiang Province

Health Commission of Zhejiang Province

Key Research and Development Program of Zhejiang Province

Publisher

Elsevier BV

Reference37 articles.

1. UniverSeg: universal medical image segmentation;Butoi;arXiv,2023

2. Segment anything;Kirillov;arXiv,2023

3. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation;Isensee;Nat Methods,2021

4. Automated segmentation of the optic disc from fundus images using an asymmetric deep learning network;Wang;Pattern Recognition,2021

5. Automated delineation of corneal layers on OCT images using a boundary-guided CNN;Wang;Pattern Recognition,2021

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