Machine learning for COVID-19 detection and prognostication using chest radiographs and CT scans: a systematic methodological review. - Michael Roberts; Derek Driggs; Matthew Thorpe; Julian D. Gilbey; Michael Yeung; Stephan Ursprung; Angelica I. Avilés-Rivero; Christian Etmann; Cathal McCague; Lucian Beer; Jonathan R. Weir-McCall; Zhongzhao Teng; et al. (CoRR, 2020)

Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. - Michael Roberts; Derek Driggs; Matthew Thorpe; Julian D. Gilbey; Michael Yeung; Stephan Ursprung; Angelica I. Avilés-Rivero; Christian Etmann; Cathal McCague; Lucian Beer; Jonathan R. Weir-McCall; Zhongzhao Teng; et al. (Nat. Mach. Intell., 2021)

Calibrating Ensembles for Scalable Uncertainty Quantification in Deep Learning-based Medical Segmentation. - Thomas Buddenkotte; Lorena Escudero Sanchez; Mireia Crispin-Ortuzar; Ramona Woitek; Cathal McCague; James D. Brenton; Ozan Öktem; Evis Sala; Leonardo Rundo (CoRR, 2022)

Calibrating ensembles for scalable uncertainty quantification in deep learning-based medical image segmentation. - Thomas Buddenkotte; Lorena Escudero Sanchez; Mireia Crispin-Ortuzar; Ramona Woitek; Cathal McCague; James D. Brenton; Ozan Öktem; Evis Sala; Leonardo Rundo (Comput. Biol. Medicine, 2023)

A Self-supervised Image Registration Approach for Measuring Local Response Patterns in Metastatic Ovarian Cancer. - Inês Prata Machado; Anna Reithmeir; Fryderyk Victor Kögl; Leonardo Rundo; Gabriel Funingana; Marika Reinius; Gift Mungmeeprued; Zeyu Gao 001; Cathal McCague; Eric Kerfoot; Ramona Woitek; Evis Sala; et al. (WBIR, 2024)

Multi-task deep learning for automatic image segmentation and treatment response assessment in metastatic ovarian cancer. - Bevis Drury; Inês Prata Machado; Zeyu Gao 001; Thomas Buddenkotte; Golnar Mahani; Gabriel Funingana; Marika Reinius; Cathal McCague; Ramona Woitek; Anju Sahdev; Evis Sala; James D. Brenton; et al. (Int. J. Comput. Assist. Radiol. Surg., 2025)