Learning with limited annotations: A survey on deep semi-supervised learning for medical image segmentation

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 169, P. 107840 - 107840

Published: Dec. 16, 2023

Language: Английский

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WORD: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from CT image

Medical Image Analysis, Journal Year: 2022, Volume and Issue: 82, P. 102642 - 102642

Published: Sept. 30, 2022

Language: Английский

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Ambiguity-selective consistency regularization for mean-teacher semi-supervised medical image segmentation

Medical Image Analysis, Journal Year: 2023, Volume and Issue: 88, P. 102880 - 102880

Published: June 28, 2023

Language: Английский

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Deep semi-supervised learning for medical image segmentation: A review

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 245, P. 123052 - 123052

Published: Jan. 4, 2024

Language: Английский

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Mutual learning with reliable pseudo label for semi-supervised medical image segmentation

Medical Image Analysis, Journal Year: 2024, Volume and Issue: 94, P. 103111 - 103111

Published: Feb. 21, 2024

Language: Английский

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SegRap2023: A benchmark of organs-at-risk and gross tumor volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma

Medical Image Analysis, Journal Year: 2025, Volume and Issue: 101, P. 103447 - 103447

Published: Jan. 2, 2025

Language: Английский

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Efficient Combination of CNN and Transformer for Dual-Teacher Uncertainty-guided Semi-supervised Medical Image Segmentation

Computer Methods and Programs in Biomedicine, Journal Year: 2022, Volume and Issue: 226, P. 107099 - 107099

Published: Sept. 2, 2022

Language: Английский

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PyMIC: A deep learning toolkit for annotation-efficient medical image segmentation

Computer Methods and Programs in Biomedicine, Journal Year: 2023, Volume and Issue: 231, P. 107398 - 107398

Published: Feb. 7, 2023

Language: Английский

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RCPS: Rectified Contrastive Pseudo Supervision for Semi-Supervised Medical Image Segmentation

IEEE Journal of Biomedical and Health Informatics, Journal Year: 2023, Volume and Issue: 28(1), P. 251 - 261

Published: Oct. 6, 2023

Medical image segmentation methods are generally designed as fully-supervised to guarantee model performance, which requires a significant amount of expert annotated samples that high-cost and laborious. Semi-supervised can alleviate the problem by utilizing large number unlabeled images along with limited labeled images. However, learning robust representation from numerous remains challenging due potential noise in pseudo labels insufficient class separability feature space, undermines performance current semi-supervised approaches. To address issues above, we propose novel method named Rectified Contrastive Pseudo Supervision (RCPS), combines rectified supervision voxel-level contrastive improve effectiveness segmentation. Particularly, design rectification strategy for based on uncertainty estimation consistency regularization reduce influence labels. Furthermore, introduce bidirectional voxel loss network ensure intra-class inter-class contrast increases The proposed RCPS has been validated two public datasets an in-house clinical dataset. Experimental results reveal yields better compared state-of-the-art medical source code is available at https://github.com/hsiangyuzhao/RCPS.

Language: Английский

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Root canal treatment planning by automatic tooth and root canal segmentation in dental CBCT with deep multi-task feature learning

Medical Image Analysis, Journal Year: 2023, Volume and Issue: 85, P. 102750 - 102750

Published: Jan. 20, 2023

Language: Английский

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