Free Meal: Boosting Semi-Supervised Polyp Segmentation by Harvesting Negative Samples

Existing semi-supervised polyp segmentation methods assume that unlabeled images are positive, containing lesions to be annotated, while neglecting negative samples that are widely available in practice. This letter reveals that harvesting lesion-free negative samples can effectively boost polyp segmentation performance. Directly extending the labeled set with negative samples is sub-optimal since it introduces potential class imbalance. To overcome this challenge, we first introduce a data augmentation strategy named TypeMix. By fusing unlabeled samples with negative samples, the network can better benefit from diverse features provided by negatives while alleviating the potential side effects. Furthermore, it is observed that the number of negative samples significantly exceeds that of lesion samples. To reduce redundancy and improve training efficiency, we propose a dynamic informativeness-aware sampling strategy, prioritizing the active selection of high-valuable negative samples. Extensive experiments on public datasets demonstrate that our simple but effective strategies are enough to consistently outperform other state-of-the-art methods, offering new possibilities for future work from a data collection perspective.