Publications

Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review

Motion represents one of the major challenges in magnetic resonance imaging (MRI). Since the MR signal is acquired in frequency space, …

Veronika Spieker, Hannah Eichhorn, Kerstin Hammernik, Daniel Rueckert, Christine Preibisch, Dimitrios C. Karampinos, Julia A. Schnabel

Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review

What do we learn? Debunking the Myth of Unsupervised Outlier Detection

Even though auto-encoders (AEs) have the desirable property of learning compact representations without labels and have been widely …

Cosmin I. Bercea, Daniel Rueckert, Julia A. Schnabel

What do we learn? Debunking the Myth of Unsupervised Outlier Detection

Improved 3D tumour definition and quantification of uptake in simulated lung tumours using deep learning

Laura Dal Toso, Zacharias Chalampalakis, Irène Buvat, Claude Comtat, Gary Cook, Vicky Goh, Julia A. Schnabel, Paul K Marsden

AtrialJSQnet: A New framework for joint segmentation and quantification of left atrium and scars incorporating spatial and shape information

Lei Li, Veronika Zimmer, Julia A. Schnabel, Xiahai Zhuang

A Deep Learning-based Integrated Framework for Quality-aware Undersampled Cine Cardiac MRI Reconstruction and Analysis

Inês P Machado, Esther Puyol-Antón, Kerstin Hammernik, Gastao Cruz, Devran Ugurlu, Ihsane Olakorede, Ilkay Oksuz, Bram Ruijsink, Miguel Castelo-Branco, Alistair A Young, others