Author

Younes Nadir

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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 · Engineering, Technology &amp... · 0 citations

Open access Sep 2026

Reproducible and Explainable Machine Learning for Breast Cancer Classification: Sensitivity-Oriented Thresholding and Independent Methodological Replication

Background: WDBC is a small historical benchmark, and near-ceiling performance alone provides limited evidence of transportability. Methods: We evaluated five model families for discrimination, calibration, and paired statistical testing. We utilized sensitivity-oriented out-of-fold (OOF) thresholds and decision curve...

Younes Nadir, M. Rachdi, Abdellah Bakhouyi et al. · Applied Sciences · 0 citations