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]
Assessing and Enhancing the Robustness of Brain Tumor Segmentation using ...
E Alwadee, X Sun, Y Qin, FC Langbein. LATUP-Net: A Lightweight 3D Attention U-Net with Parallel Convolutions for Brain Tumor Segmentation. Preprint, 2024. [arxiv:2404.05911] [PDF]
LATUP-Net: A Lightweight 3D Attention U-Net with Parallel Convolutions for ... Recently updated!
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]
Metabolite Quantification with AI from MR Spectra
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]
Robustness of Dynamic Quantum Control: Differential Sensitivity Bounds
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. Preprint, 2023. [arXiv:2401.00294] [PDF]
Robust Quantum Control in Closed and Open Systems: Theory and ...
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/]
Interview – 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]
Quantification of Metabolites in Magnetic Resonance Spectra with Deep Learning ...
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]
Disturbance-agnostic robust performance with structured uncertainties and initial state error ...
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]
Control and Machine Learning for Magnetic Resonance Spectroscopy
I Khalid, CA Weider, EA Jonckheere, SG Shermer, FC Langbein. Sample-efficient Model-based Reinforcement Learning for Quantum Control. Phys. Rev. Research 5, 043002, 2023. [DOI:10.1103/PhysRevResearch.5.043002] [arXiv:2304.09718] [PDF]
Sample-efficient Model-based Reinforcement Learning for Quantum Control
Irtaza Khalid, Carrie Weidner, S.G. Schirmer, Edmond Jonckheere, Frank C. Langbein. Sample-efficient Model-based Reinforcement Learning for Quantum Control. Poster, BQIT:23, 2023. [PDF:poster]