Adaptive Annotation Correlation Based Multi-Annotation Learning for Calibrated Medical Image Segmentation.
IEEE J Biomed Health Inform
; PP2024 Aug 28.
Article
de En
| MEDLINE
| ID: mdl-39196744
ABSTRACT
Medical image segmentation is a fundamental task in many clinical applications, yet current automated segmentation methods rely heavily on manual annotations, which are inherently subjective and prone to annotation bias. Recently, modeling annotator preference has garnered great interest, and several methods have been proposed in the past two years. However, the existing methods completely ignore the potential correlation between annotations, such as complementary and discriminative information. In this work, the Adaptive annotation CorrelaTion based multI-annOtation LearNing (ACTION) method is proposed for calibrated medical image segmentation. ACTION employs consensus feature learning and dynamic adaptive weighting to leverage complementary information across annotations and emphasize discriminative information within each annotation based on their correlations, respectively. Meanwhile, memory accumulation-replay is proposed to accumulate the prior knowledge and integrate it into the model to enable the model to accommodate the multi-annotation setting. Two medical image benchmarks with different modalities are utilized to evaluate the performance of ACTION, and extensive experimental results demonstrate that it achieves superior performance compared to several state-of-the-art methods.
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Langue:
En
Journal:
IEEE J Biomed Health Inform
Année:
2024
Type de document:
Article
Pays de publication:
États-Unis d'Amérique