arXiv:2602.06938v2 Announce Type: replace-cross Abstract: The classification performance of deep neural networks relies strongly on access to large, accurately annotated datasets. In medical imaging, however, obtaining such datasets is particularly challenging since annotations must be provided by specialized physicians, which severely limits the pool of annotators. Furthermore, class boundaries can often be ambiguous or difficult to define which further complicates machine learning-based classification. In this paper, we want to address this problem and introduce a framework for mislabel dete
The increasing reliance on deep neural networks across various fields, particularly medical imaging, has exposed the critical vulnerability of model performance to data quality and accurate annotation, leading to focused research on robust data validation.
This paper addresses a fundamental challenge in AI deployment, particularly in sensitive sectors like healthcare, by improving model reliability and reducing the financial and ethical risks associated with mislabeled data.
The proposed framework offers a systematic approach to identify and correct mislabeled data, potentially leading to more accurate and trustworthy AI models, especially in domains scarce of expert annotators.
- · AI developers
- · Medical AI companies
- · Healthcare providers
- · Patients
- · Companies with poor data governance
- · Inefficient annotation services
Improved accuracy and reliability of medical AI diagnostic tools.
Reduced physician workload in data annotation and validation, allowing more focus on patient care.
Accelerated adoption and trust in AI-powered medical diagnostics, leading to better patient outcomes and potentially lower healthcare costs.
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Read at arXiv cs.LG