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Open access Oct 2026

The Reproducibility Gap in Mammographic CAD: Quantifying Patient-Level Leakage Effects on CBIS-DDSM

Studies with deep learning models for mammographic breast cancer classification routinely report AUC values exceeding 0.90 on public benchmarks; however, the validity of these results is rarely examined at the protocol level. A pervasive issue is patient-level data leakage, whereby images from the same patient appear i...

Chaima Elmejgari, Younes Nadir, M. Qbadou · 0 citations

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