Forschungsleiter, Artificial Intelligence in Neuroimaging

PD, PhD

Biosketch

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

• PhD in Computer Science, University of Bath, UK

• SNF Ambizione Fellow 2010-2014

Research interests

• Novel applications of machine learning to brain MRI

• Stroke Imaging, in particular perfusion MRI

• Education of MDs in artificial intelligence

Currently funded projects

• HORAO (2022). SNF Synergia, together with with Phillipe Schucht (Neurosurgery), Tatiana Novikova (optical imaging) , Ekkehard Hewer (pathology)

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

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

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

Team

Diego Zeiter (Reserach assistant, ASAP)

Christopher Hahne (Postdoc, 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