Z Ma, O Karakus, SM Shermer, FC Langbein. The Impact of Training Data on MRS Metabolite Quantification with Deep Learning. Poster. ISMRM & ISMRT Annual Meeting & Exhibition, Honolulu, Hawaiʻi, USA, 10-15 May 2025. [PDF:Abstract]
CA Weidner, SP O’Neil, EA Jonckheere, FC Langbein, SG Schirmer. Energy Landscape Shaping for Robust Control of Atoms in Optical Lattices. Preprint, 2025. [arXiv:2501.12564] [PDF]
Asmail Muftah, SM Shermer, Frank C Langbein. Texture Feature Analysis for Classification of Early-Stage Prostate Cancer in mpMRI. Proc AI in Healthcare (AIiH), Swansea, UK, September 2024. [PDF] [arXiv:2406.15571]
E Alwadee, X Sun, Y Qin, FC Langbein. Assessing and Enhancing the Robustness of Brain Tumor Segmentation using a Probabilistic Deep Learning Architecture. Proc ISMRM and ISMRT Annual Meeting and Exhibition, Singapore, May 2024. [PDF] [Slides:PDF] [Video]
E Alwadee, X Sun, Y Qin, FC Langbein. LATUP-Net: A Lightweight 3D Attention U-Net with Parallel Convolutions for Brain Tumor Segmentation. Computers in Biology and Medicine, 184:109353, 2025. [DOI:10.1016/j.compbiomed.2024.109353] [arXiv:2404.05911] [PDF]
Frank C Langbein. Metabolite Quantification with AI from MR Spectra. Cardiff/University of Chinese Academy of Sciences (UCAS) Workshop on Visual Computing, 29th January 2024. [PDF]
S. P. O’Neil, C. A. Weidner, E. A. Jonckheere, F. C. Langbein, S. G. Schirmer. Robustness of Dynamic Quantum Control: Differential Sensitivity Bounds. AVS Quantum Sci. 6, 032001, 2024. [DOI:10.1116/5.0196110] [arXiv:2401.00301] [PDF]
C. A. Weidner, E. A. Reed, J. Monroe, S. O’Neil, E. Maas, E. A. Jonckheere, F. C. Langbein, S. G. Schirmer. Robust Quantum Control in Closed and Open Systems: Theory and Practice. Automatica, 172:111987, 2025. [DIO:10.1016/j.automatica.2024.111987] [arXiv:2401.00294] [PDF]
Finding and Characterising Robust Quantum Controls. Faculti interview with Frank Langbein on robust quantum control, 25th May 2023. [URL: https://faculti.net/finding-and-characterising-robust-quantum-controls/]
Zien Ma, Oktay Karakus, Sophie Shermer, Frank Langbein. Quantification of Metabolites in Magnetic Resonance Spectra with Deep Learning: Insights on Simulated and Real Data. Presentation at the One Day Meeting: Synthetic Data for Machine Learning, The British Machine Vision Association and Society for Pattern Recognition, Wednesday 8 November 2023. [PDF:abstract]
E Jonckheere, SG Schirmer, FC Langbein, CA Weidner, S O’Neil. Disturbance-agnostic robust performance with structured uncertainties and initial state error in classical versus quantum oscillatory systems. Preprint, 2023. [arXiv:2305.03918] [PDF]
FC Langbein. Control and Machine Learning for Magnetic Resonance Spectroscopy. Keynote talk, Frontiers of Intelligent Computing: Theory and Applications (FICTA), 11-12 April 2023. [Slides]