From Theory to Decision Rule: Calibrating the Noisy-Label Crossover for Vision-Language Model Weak Supervision Across Three Medical-Imaging Benchmarks
arXiv:2605.24771v1 Announce Type: cross Abstract: Classical noisy-label theory predicts that downstream performance under weak supervision is bounded above by the labeler's accuracy, implying a sharp crossover: once a gold-trained classifier matches the labeler, weak labels stop helping and start hurting. The prediction is theoretical; what is missing is a benchmark calibration that turns it into an instance-level statement for modern foundation-model labelers. We provide such a calibration for BiomedCLIP-generated weak labels on three medical-imaging benchmarks (PCAM, ISIC, NIH-CXR) and six d
The proliferation of powerful foundation models necessitates rigorous calibration of their weak supervision capabilities, especially in sensitive domains like medical imaging, driving immediate research in this area.
This research provides crucial empirical validation for applying vision-language models in weak supervision, directly impacting the accuracy and reliability of AI-assisted medical diagnostics.
Understanding the 'noisy-label crossover' for modern foundation models allows for better deployment strategies for AI in medical imaging, moving from theoretical prediction to instance-level application.
- · Medical AI developers
- · Healthcare providers
- · Patients
- · AI-assisted diagnostics
- · Traditional medical imaging interpretations
- · AI models with uncalibrated weak supervision
More accurate and efficient AI models for medical image analysis.
Accelerated development and adoption of AI tools in clinical settings due to increased trustworthiness.
Potential for early disease detection and personalized treatment pathways to improve public health outcomes globally.
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Read at arXiv cs.LG