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

arXiv:2306.12189v2 Announce Type: replace
Abstract: 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. …

abstract annotation arxiv biomedical class classification cs.cv current data gap general image images issue paper quality quality data resolution semi-supervised semi-supervised learning solutions strategies strategy struggle supervised learning type validation world