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Generating sufficient labeled data is a significant hurdle in the efficientexecution of deep learning projects, especially in uncharted territories ofimage segmentation where labeling demands extensive time, unlike classificationtasks. Our study confronts this challenge, operating in an environmentconstrained by limited hardware resources and the lack of extensive datasets orpre-trained models. We introduce the novel use of Inconsistency Masks (IM) toeffectively filter uncertainty in image-pseudo-label pairs, substantiallyelevating segmentation quality beyond traditional semi-supervised learningtechniques. By integrating IM with other methods, we demonstrate remarkablebinary segmentation performance on the ISIC 2018 dataset, starting with just10% labeled data. Notably, three of our hybrid models outperform those trainedon the fully labeled dataset. Our approach consistently achieves exceptionalresults across three additional datasets and shows further improvement whencombined with other techniques. For comprehensive and robust evaluation, thispaper includes an extensive analysis of prevalent semi-supervised learningstrategies, all trained under identical starting conditions. The full code isavailable at: https://github.com/MichaelVorndran/InconsistencyMasks

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