Rectified Mixed-Label Learning for Semi-Supervised Medical Image Segmentation

Semi-supervised medical image segmentation (SSMIS) has gained increasing attention due to its potential to alleviate the manual annotation burden. However, existing works face two key challenges: i) how to deal with the information loss caused by learning labeled and unlabeled data in an inconsisten...

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Bibliographic Details
Published in:Proceedings (IEEE International Conference on Multimedia and Expo) pp. 1 - 6
Main Authors: An, Zeyu, Chen, Zichong
Format: Conference Proceeding
Language:English
Published: IEEE 30.06.2025
Subjects:
Biomedical imaging
Contrastive learning
Data models
Faces
Image segmentation
Manuals
Medical Image Segmentation
Mixed-Label Supervision
Noise
Prototype Learning
Prototypes
Reliability
Semantics
ISSN:1945-788X
Online Access:Get full text
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Summary:Semi-supervised medical image segmentation (SSMIS) has gained increasing attention due to its potential to alleviate the manual annotation burden. However, existing works face two key challenges: i) how to deal with the information loss caused by learning labeled and unlabeled data in an inconsistent manner and ii) how to reduce the impact of label noise derived by the model's cognitive bias. To address these challenges, we propose the Rectified Mixed-label Learning (RML) method for SSMIS. First, we mix labeled and unlabeled images, and then encourage the model to learn common semantics from mixed images straightly. More importantly, we conduct mixed-label learning in both the image and feature levels to fully exploit mixed images. In detail, a rectified supervision objective is implemented at the image level, which adaptively enhances high-quality pseudo-labels while weakening unreliable pseudo-labels. Moreover, for ambiguous voxels, we guide them to acquire reliable semantic information from high-quality prototypes in the feature space, thereby improving the identification of unreliable regions. Numerous experimental results demonstrate the superiority of our proposed method over previous SoTA methods.
ISSN:1945-788X
DOI:10.1109/ICME59968.2025.11210078