Dataset-Origin Signatures and Shortcut Learning in Screening Mammography AI: A Cross-Dataset Case Study
A new study demonstrates that adding biopsy-confirmed, abnormal-enriched cases from external datasets to screening mammography AI training can significantly reduce model performance, with AUC-ROC dropping from 0.737 to as low as 0.620. The performance decline is linked to persistent dataset-specific characteristics that dominate learned representations, even after consistent preprocessing. The research underscores that naive pooling of heterogeneous mammography datasets introduces domain shifts that can outweigh the benefits of increased positive cases.
Why it matters: This work highlights that hidden dataset biases can undermine AI reliability in medical imaging, emphasizing the need for domain-aware strategies when combining data from different sources.
Full story at: arXiv Computer Vision ↗