arXiv:2607.05965v1 Announce Type: cross Abstract: Vascular computed tomography datasets are commonly annotated only once per scan, yielding the pervasive yet under addressed problem of single mask annotation noise. Existing solutions either require costly multirater fusion or are coupled with network training, preventing explicit auditing of where and why labels fail. We introduce a decoupled framework for single-mask annotation noise detection that leverages cross-sectional patch self-consistency to produce interpretable and auditable noise evidence. Tubular anatomy exhibits strong cross-sect
The increasing complexity and volume of medical imaging annotations, especially for AI training, necessitate more robust and reliable noise detection methods without incurring prohibitive costs or dependency on network training.
Accurate medical image annotation is critical for the development of reliable AI diagnostic tools, and methods to detect annotation noise improve model robustness and reduce downstream errors.
This decoupled framework allows for explicit auditing and quality control of single-mask annotations, providing interpretability that was previously difficult or impossible with coupled or multi-rater systems.
- · Medical AI developers
- · Healthcare providers
- · Annotation services
- · Inefficient annotation pipelines
- · AI models trained on faulty data
Improved reliability and auditability of AI systems based on medical imaging.
Faster and cheaper development cycles for medical AI, making advanced diagnostics more accessible.
Enhanced trust in AI-powered medical diagnoses, accelerating their integration into clinical practice and improving patient outcomes.
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Read at arXiv cs.AI