dataset-shift

Frouros: an open-source Python library for drift detection in machine learning systems.

Code for 'Automatic dataset shift identification to support root cause analysis of AI performance drift', MICCAI 2025

A flexible and powerful Python library for dataset shift analysis and characterization, providing supervised and unsupervised evaluation of temporal and multi-source data shifts, visualization tools, and statistical insights for data integrity and model performance monitoring

Code for "Distance Matters for Improving Performance Estimation Under Covariate Shift", ICCV Workshop on Uncertainty Quantification 2023, Roschewitz & Glocker.

statistical tests for drift detection and dataset shift

Code for the paper "Where are we with calibration under dataset shift in image classification?"

Reliability-first cardiovascular AI with patient-disjoint HPO, selective prediction, calibration stress tests, and cross-dataset transportability auditing.

Beyond Random Tile Splits: Source-Held-Out Evaluation and Multi-Stream Fusion for Colorectal Histology. Held-out-source evaluation of color, traditional CV, CNN, and foundation-model (Phikon-v2, ConvNeXt-Tiny) pipelines on Kather-5k colorectal histology. Macro F1 drops from 0.98 to 0.82 when whole source images are held out.

Genome-anchored calibration of a continuous phenotype across cohorts, with uncertainty-propagated probabilistic labels

A benchmark postmortem on synthetic shortcuts, near-duplicates, and distribution shift. 2nd place at Purple Hack 2026.

Shift type, not magnitude, determines ML failure modes under deployment shift — a cross-domain audit protocol and benchmark