Improving Pseudo-Labels Selection Using Domain Priors for Semi-Supervised Detection in Capsule Endoscopy

The unavailability of expert annotations for learning deep models for pathology detection in Wireless Capsule Endoscopy (WCE) has led to an increase in explorations of semi-supervised learning. Semi-supervised models reduce the dependency on large-scale annotations. However, the resulting quality of representations relies on the quality of pseudo-labels generated from unlabeled data. In this work, we develop domain-specific augmentations in conjunction with weighted box fusion and active sampling to better select the unlabeled samples and reduce annotation cost in WCE. We demonstrate the improvement in performance using the proposed idea on two different datasets including SEE-AI and Kvasir-Capsule, with multiple standard metrics like average precision and its variants. The experimental results reveal that domain-specific augmentations with active sampling can help in selecting the most informative unlabeled samples, making it possible to improve semi-supervised models. With an annotation cost of only 35% of the actively selected unlabeled data, our method performs better than the state-of-the-art models on the same datasets for both fully supervised Faster-RCNN and Semi-supervised Unbiased Teacher and Active Teacher. We achieved a gain of +3.09% in AP50 on SEE-AI and +3.37% in AP75 on Kvasir-Capsule over the Active Teacher model. Our code is available at https://github.com/agossouema2011/SSOD_With_DTA_WCE.