Annotating Ambiguous Images: General Annotation Strategy for High-Quality Data with Real-World Biomedical Validation
arxiv(2023)
摘要
In the field of image classification, existing methods often struggle with
biased or ambiguous data, a prevalent issue in real-world scenarios. Current
strategies, including semi-supervised learning and class blending, offer
partial solutions but lack a definitive resolution. Addressing this gap, our
paper introduces a novel strategy for generating high-quality labels in
challenging datasets. Central to our approach is a clearly designed flowchart,
based on a broad literature review, which enables the creation of reliable
labels. We validate our methodology through a rigorous real-world test case in
the biomedical field, specifically in deducing height reduction from vertebral
imaging. Our empirical study, leveraging over 250,000 annotations, demonstrates
the effectiveness of our strategies decisions compared to their alternatives.
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