6 papers
NOAH: Learning the Full Patient Journey. A Longitudinal Multimodal Time-Aware Model for Representation and Forecasting
Tobias Susetzky, Raphael Rehms, Dmitrii Seletkov +5
The digitization of healthcare has generated vast, longitudinal, and multimodal patient records over a lifetime, yet fully exploiting these data to represent and predict patient st…
Echo2ECG: Enhancing ECG Representations with Cardiac Morphology from Multi-View Echos
Michelle Espranita Liman, Özgün Turgut, Alexander Müller +3
Electrocardiography (ECG) is a low-cost, widely used modality for diagnosing electrical abnormalities like atrial fibrillation by capturing the heart's electrical activity. However…
Multi-View Stenosis Classification Leveraging Transformer-Based Multiple-Instance Learning Using Real-World Clinical Data
Nikola Cenikj, Özgün Turgut, Alexander Müller +6
Coronary artery stenosis is a leading cause of cardiovascular disease, diagnosed by analyzing the coronary arteries from multiple angiography views. Although numerous deep-learning…
Global and Local Contrastive Learning for Joint Representations from Cardiac MRI and ECG
Alexander Selivanov, Philip Müller, Özgün Turgut +2
An electrocardiogram (ECG) is a widely used, cost-effective tool for detecting electrical abnormalities in the heart. However, it cannot directly measure functional parameters, suc…
OTIS: Learning High-Quality Time Series Features With Tiny Encoders
Özgün Turgut, Philip Müller, Martin J. Menten +1
We introduce OTIS, an open time series encoder that yields high-quality time series features for downstream deployment on any system, including resource-constrained wearables and i…
Estimating Neural Orientation Distribution Fields on High Resolution Diffusion MRI Scans
Mohammed Munzer Dwedari, William Consagra, Philip Müller +3
The Orientation Distribution Function (ODF) characterizes key brain microstructural properties and plays an important role in understanding brain structural connectivity. Recent wo…