Hidden Stratification Causes Clinically Meaningful Failures in Machine Learning for Medical Imaging.¶
Oakden-Rayner, L., Dunnmon, J., Carneiro, G., & Re, C. (2020). Hidden Stratification Causes Clinically Meaningful Failures in Machine Learning for Medical Imaging. Proceedings of the ACM Conference on Health, Inference, and Learning, 151-159.
Cited by¶
1 citation across 1 artifact.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Ground Truth
- But the prime's warning about the reference's own error structure is concrete and consequential: if the "ground truth" for a chest-X-ray model is radiologist consensus rather than confirmed disease status, the model is trained and scored to reproduce radiologists' judgments, including their systematic misses
This sourceShows medical-imaging models scored against radiologist-consensus labels reproduce systematic reader errors rather than true disease status.
- But the prime's warning about the reference's own error structure is concrete and consequential: if the "ground truth" for a chest-X-ray model is radiologist consensus rather than confirmed disease status, the model is trained and scored to reproduce radiologists' judgments, including their systematic misses
Verification¶
This reference passed the adversarial substantiation pipeline: it was checked to exist and to support the claim it is attached to. See how references were verified.
Registry ID ref:ac7c5ade7450 · see in the full table