Dual uncertainty-guided multi-model pseudo-label learning for semi-supervised medical image segmentation.
Math Biosci Eng
; 21(2): 2212-2232, 2024 Jan 11.
Article
in En
| MEDLINE
| ID: mdl-38454680
ABSTRACT
Semi-supervised medical image segmentation is currently a highly researched area. Pseudo-label learning is a traditional semi-supervised learning method aimed at acquiring additional knowledge by generating pseudo-labels for unlabeled data. However, this method relies on the quality of pseudo-labels and can lead to an unstable training process due to differences between samples. Additionally, directly generating pseudo-labels from the model itself accelerates noise accumulation, resulting in low-confidence pseudo-labels. To address these issues, we proposed a dual uncertainty-guided multi-model pseudo-label learning framework (DUMM) for semi-supervised medical image segmentation. The framework consisted of two main parts The first part is a sample selection module based on sample-level uncertainty (SUS), intended to achieve a more stable and smooth training process. The second part is a multi-model pseudo-label generation module based on pixel-level uncertainty (PUM), intended to obtain high-quality pseudo-labels. We conducted a series of experiments on two public medical datasets, ACDC2017 and ISIC2018. Compared to the baseline, we improved the Dice scores by 6.5% and 4.0% over the two datasets, respectively. Furthermore, our results showed a clear advantage over the comparative methods. This validates the feasibility and applicability of our approach.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Research Design
/
Image Processing, Computer-Assisted
Language:
En
Journal:
Math Biosci Eng
Year:
2024
Document type:
Article
Affiliation country:
China