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.

翻译：基于深度学习的技术在提供足够像素级标注数据的情况下，已被证明在息肉分割任务中有效。然而，手动标注的高昂成本已成为模型泛化的瓶颈。为降低标注成本，我们提出了一种深度主动学习框架，用于实现标注高效的息肉分割。在实践中，我们通过检查息肉预测图掩膜特征与背景区域特征之间的相似性，来度量每个样本的不确定性。由于分割模型在处理前景与背景特征难以区分的样本时表现较弱，不确定性采样有助于拟合未充分学习的数据。此外，通过不确定性加权的图像级特征聚类，能够识别既不确定又具代表性的样本。为增强主动选择策略的选择性，我们提出了一种新颖的无监督特征差异学习机制。选择策略与特征优化协同作用，在有限的标注预算下实现最优性能。大量实验结果表明，在公共数据集和大型内部数据集上，我们的方法相较于其他竞争方法均取得了最先进的性能。