Richard McKinley


Senior Researcher, Artificial Intelligence in Medical imaging



Bachelor/Master degree in Mathematics, University of Cambridge, UK

PhD in Computer Science, University of Bath, UK


Research interests

Novel applications of machine learning to brain MRI

Stroke Imaging, in particular perfusion MRI

Applications of medical image analysis to biomedical optical imaging in neurosurgery

Education of MDs in artificial intelligence


Currently funded projects

• HORAO (2022). SNF Synergia (with Phillipe Schucht, Tatiana Novikova, Ekkehard Hewer)

   o Towards in-vivo detection of myelinated fibre tracts and discrimination between healthy and tumor tissue using polarized light (Mueller Polarimetry).

• CAIM fund (2022). Funding from Bern Centre for Artificial Intelligence (with Piotr Radojewski)

   o Lesion detection and lesion dynamics in patients with multiple sclerosis: focus on methods robust to differences in scanner, sequence and image resolution.

• Advanced Stroke Analytics Platform (2021). Innosuisse (with Roland Wiest, Tobi Kober, Jonas Ricciardi)

   o Federated Learning across medical centres to better detect tissue damage and predict patient outcomes in acute ischemic stroke.



Ivan Diaz (CAIM Fund project)

Sebastian Otálora (ASAP)

Stefano Moriconi (HORAO)


Prizes and awards

Dr. McKinley has led the development of several winning methods in MICCAI biomedical imaging challenges, in particular first place for segmentation uncertainty quantification and first place for survival prediction in the 2020 BRATS challenge and first place for MS lesion segmentation in the 2016 MSSEG challenge.


Selected publications

McKinley, Wepfer, Aschwanden, Grunder, Muri, Rummel, Verma, Weisstanner, Reyes, Salmen, Chan, Wagner, & Wiest. Simultaneous Lesion and Neuroanatomy Segmentation in Multiple Sclerosis Using Deep Neural Networks. Scientific Reports, 11:1087, 2021.

Rebsamen, Rummel, Reyes, Wiest, & McKinley. Direct cortical thickness estimation using deep learning-based anatomy segmentation and cortex parcellation, Human Brain Mapping, 2020

McKinley, Häni, Gralla, El-Koussy, Bauer, Arnold, Fischer, Jung, Mattmann, Reyes, & Wiest. Fully Automated Stroke Tissue Estimation Using Random Forests (FASTER), Journal of Cerebral Blood Flow and Metabolism, 2016

Publications in Google Scholar



Copyright (c) Scancore 2018. All rights reserved.