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Department of Orthopaedic Surgery

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#artificial intelligence Preprint Oct 2026

Diagnosing Low-Confidence Patient Profiles via Localized Harmful Covariate Shift Detection

Before deploying clinical prediction models, their reliability must be assessed to ensure safe use. Errors may arise from distribution shifts between training and deployment or from heterogeneous performance where the model systematically underperforms for certain patient subgroups. Yet most post-hoc diagnostic methods...

Olivier Lefebvre, Hakima Laribi, D. Poenaru et al. · 0 citations

Preprint Aug 2026

Can Unsupervised Methods Outperform Supervised Deep Learning When Ground Truth Is Sparse? A Case Study of Bronchovascular Bundle Segmentation in Low-Dose CT

Background Lung cancer remains the deadliest cancer worldwide because it is often diagnosed too late. Effective treatment depends on detection at an early screening stage. However, the growing number of patients and the limited number of radiologists lead to prolonged diagnostic waiting times. In very early stage lung...

A. Mrukwa, M. Socha, A. Suwalska et al. · 0 citations