5 papers
TSPFN: A Temporal Tabular Foundation Model for Physiological Time Series Classification
Jérémie Stym-Popper, Clément Rambour, Federica Granese +2
Designing models that generalize effectively in low- to medium-data regimes remains a primary challenge in medical machine learning, particularly for physiological time-series clas…
Are Tabular Foundation Models Robust to Realistic Query Distribution Shifts in Microbiome Data?
Giulia Perciballi, Ahmad Fall, Federica Granese +2
Tabular foundation models (TFMs) achieve strong performance on microbiome abundance data, yet their robustness under realistic distribution shift remains poorly characterized. We i…
IKrNet: A Neural Network for Detecting Specific Drug-Induced Patterns in Electrocardiograms Amidst Physiological Variability
Ahmad Fall, Federica Granese, Alex Lence +5
Monitoring and analyzing electrocardiogram (ECG) signals, even under varying physiological conditions, including those influenced by physical activity, drugs and stress, is crucial…
ECGtizer: a fully automated digitizing and signal recovery pipeline for electrocardiograms
Alex Lence, Ahmad Fall, Samuel David Cohen +4
Electrocardiograms (ECGs) are essential for diagnosing cardiac pathologies, yet traditional paper-based ECG storage poses significant challenges for automated analysis. This study…
ECGrecover: a Deep Learning Approach for Electrocardiogram Signal Completion
Alex Lence, Federica Granese, Ahmad Fall +4
In this work, we address the challenge of reconstructing the complete 12-lead ECG signal from its incomplete parts. We focus on two main scenarios: (i) reconstructing missing signa…