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In the field of image classification, existing methods often struggle withbiased or ambiguous data, a prevalent issue in real-world scenarios. Currentstrategies, including semi-supervised learning and class blending, offerpartial solutions but lack a definitive resolution. Addressing this gap, ourpaper introduces a novel strategy for generating high-quality labels inchallenging datasets. Central to our approach is a clearly designed flowchart,based on a broad literature review, which enables the creation of reliablelabels. We validate our methodology through a rigorous real-world test case inthe biomedical field, specifically in deducing height reduction from vertebralimaging. Our empirical study, leveraging over 250,000 annotations, demonstratesthe effectiveness of our strategies decisions compared to their alternatives.

Annotating Ambiguous Images: General Annotation Strategy for High-Quality Data with Real-World Biomedical Validation