Inconsistency Masks: Removing the Uncertainty from Input-Pseudo-Label Pairs

Generating sufficient labeled data is a significant hurdle in the efficient execution of deep learning projects, especially in uncharted territories of image segmentation where labeling demands extensive time, unlike classification tasks. Our study confronts this challenge, operating in an environment constrained by limited hardware resources and the lack of extensive datasets or pre-trained models. We introduce the novel use of Inconsistency Masks (IM) to effectively filter uncertainty in image-pseudo-label pairs, substantially elevating segmentation quality beyond traditional semi-supervised learning techniques. By integrating IM with other methods, we demonstrate remarkable binary segmentation performance on the ISIC 2018 dataset, starting with just 10% labeled data. Notably, three of our hybrid models outperform those trained on the fully labeled dataset. Our approach consistently achieves exceptional results across three additional datasets and shows further improvement when combined with other techniques. For comprehensive and robust evaluation, this paper includes an extensive analysis of prevalent semi-supervised learning strategies, all trained under identical starting conditions. The full code is available at: https://github.com/MichaelVorndran/InconsistencyMasks