Annotation-Efficient Polyp Segmentation via Active Learning

TL;DR

This paper introduces an active learning framework for polyp segmentation that reduces annotation costs by selecting the most informative samples using uncertainty and feature discrepancy measures, achieving state-of-the-art results.

Contribution

The paper presents a novel active learning approach combining uncertainty sampling and feature discrepancy learning for efficient polyp segmentation with limited annotations.

Findings

Achieved state-of-the-art performance on public and in-house datasets.

Reduced annotation effort while maintaining high segmentation accuracy.

Demonstrated effectiveness of uncertainty and feature discrepancy strategies.

Abstract

Deep learning-based techniques have proven effective in polyp segmentation tasks when provided with sufficient pixel-wise labeled data. However, the high cost of manual annotation has created a bottleneck for model generalization. To minimize annotation costs, we propose a deep active learning framework for annotation-efficient polyp segmentation. In practice, we measure the uncertainty of each sample by examining the similarity between features masked by the prediction map of the polyp and the background area. Since the segmentation model tends to perform weak in samples with indistinguishable features of foreground and background areas, uncertainty sampling facilitates the fitting of under-learning data. Furthermore, clustering image-level features weighted by uncertainty identify samples that are both uncertain and representative. To enhance the selectivity of the active selection strategy, we propose a novel unsupervised feature discrepancy learning mechanism. The selection strategy and feature optimization work in tandem to achieve optimal performance with a limited annotation budget. Extensive experimental results have demonstrated that our proposed method achieved state-of-the-art performance compared to other competitors on both a public dataset and a large-scale in-house dataset.