Sam-Correction: Fully Adaptive Label Noise Reduction for Medical Image Segmentation

Abstract

The quality of the teacher label has a dramatic impact on the outcome of the machine learning process. Medical image segmentation often suffers from a noisy ground truth (GT) mask due to difficulties in consistent annotation. Although a recent study has proposed a promising method to curate the noisy GT mask, this requires additional well-curated GT masks that are not always accessible without the aid of multiple medical experts. As an alternative, we propose SAM-Correction, which utilizes a foundation model to correct the noisy GT mask in a fully adaptive manner instead of manually preparing well-curated masks. In our approach, we progressively mitigate label noise via the segment anything model (SAM), where prompts are given from another segmentation network that we train concurrently. Experiments with various types of label noise derived from actual human annotations demonstrate that SAM-C achieves precise noise reduction and high-performing segmentation models trained thereby. Our method paves the way for robust model training even when clean labels are entirely unavailable.

arXiv comment5 pages, accepted to IEEE International Symposium on Biomedical Imaging (ISBI) 2024

References in corpus (6)

• Early-Learning Regularization Prevents Memorization of Noisy Labels
• A Probabilistic U-Net for Segmentation of Ambiguous Images
• Segment Anything in High Quality
• Disentangling Human Error from the Ground Truth in Segmentation of Medical Images
• AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encoder
• Learning to Segment from Noisy Annotations: A Spatial Correction Approach