Research

My current research activities are mainly related to machine learning for medical data analysis, with a particular focus on medical imaging.

I'm co-PI in the project Computational medical imaging and machine learning – methods, infrastructure and applications in the new Medical Imaging and Visualization Centre at the Department of Radiology, Haukeland University Hospital, funded by the Bergen Research Foundation. The project involves many researchers in Bergen, both clinical and methodological, in addition to national and international collaborators from research institutions in the USA (Mayo Clinic), Switzerland (ETH), Germany (Zuse Institute), France (ISIMA), Luxembourg (LIH) and Poland (TUL).

I'm also a member of the project Precision imaging in gynecologic cancer at MMIV, led by prof. dr. med. Ingfrid Haldorsen. The aim of the project is to integrate imaging biomarkers into clinically relevant treatment algorithms for gynecologic cancers.

Through the MobiFORSK programme I'm leading a project on machine learning together with NordicNeuroLab, a company providing products and solutions for functional MR imaging.


I'm a member of Computer Science: Software Engineering, Sensor Networks and Engineering Computing at the Western Norway University of Applied Sciences, and a member of the multidisciplinary research group Idrett, Helse og Funksjon (Sports, health and function) at HVL.

I'm a "Secondary Proposer" for the COST action "Magnetic Resonance Imaging Biomarkers for Chronic Kidney Disease" (PARENCHIMA, OC-2016-1-20493).


Supervision

I'm currently supervising the PhD candidate Samaneh Abolpour Mofrad, working on machine learning for medical data analysis, and four MSc students doing projects within the same area. See Teaching for more details.


Publications

An overview of deep learning in medical imaging focusing on MRI
with A. Lundervold, To appear in Zeitschrift für Medizinische Physik, 2018
Available online (open access) here. (arXiv version here)
Transfer learning for medical images: a case study
with S. Kaliyugarasan. Poster at GTC Europe 2018, Munich, Germany, Oct. 2018
Fast estimation of kidney volumes and time courses in DCE-MRI using convolutional neural networks
with K. Sprawka, A. Lundervold. Scientific Paper at ECR 2018, Austria Center Vienna, Austria, Feb. 2018
​​Fast semi-supervised segmentation of the kidneys in DCE-MRI using convolutional neural networks and transfer learning
with A. Lundervold, J. Rørvik. Functional Renal Imaging: Where Physiology, Nephrology, Radiology and Physics Meet, Max Delbrück Communications Center, Berlin, Oct. 2017
​​"Deep learning" i medisin
HMT, 2017/4
Python-based software for medical imaging and machine learning — an example from brain imaging in IBS
with K. Le Cornec, O. Verdier, V. Barra, and A. Lundervold, Abstract and poster at MedViz 2016
Predicting irritable bowel syndrome (IBS) from brain MR imaging data using machine learning
with A. Lundervold, E. A. Valestrand, T. Hausken, Poster at 2017 Geilo Winter School in eScience
Post-Lie algebras and isospectral flows
with K. Ebrahimi-Fard, I. Mencattini, H.Z. Munthe-Kaas Symmetry, Integrability and Geometry: Methods and Applications (SIGMA), 11, 093, 2015
arXiv version
On the Lie enveloping algebra of a post-Lie algebra
with K. Ebrahimi-Fard and H.Z. Munthe-Kaas, Journal of Lie Theory, Vol. 25, No. 4, 1139--1165, 2015
arXiv version
On algebraic structures of numerical integration on vector spaces and manifolds
with H.Z. Munthe-Kaas, IRMA Lectures in Mathematics and Theoretical Physics Vol. 21, 2015
arXiv version
Noncommutative Bell polynomials, quasideterminants and incidence Hopf algebras
with K. Ebrahimi-Fard and D. Manchon, International Journal of Algebra and Computation, Volume 24, Issue 5, 2014
arXiv version
On post-Lie algebras, Lie–Butcher series and moving frames
with H.Z. Munthe-Kaas, Foundations of Computational Mathematics, Volume 13, Issue 4, 2013
arXiv version
Backward error analysis and the substitution law for Lie group integrators
with H.Z. Munthe-Kaas, Foundations of Computational Mathematics, Volume 13, Issue 2, 2013
arXiv version
Algebraic structure of stochastic expansions and universally accurate simulation
with K. Ebrahimi-Fard, S.J.A. Malham, H.Z. Munthe-Kaas, A. Wiese, Proceedings of the Royal Society. Mathematical, Physical and Engineering Sciences, Volume 468 (2144), 2012
arXiv version

Preprints and in preparation

In prep: Fast estimation of kidney volumes and time courses in DCE-MRI using convolutional neural networks
with K. Sprawka and A. Lundervold

Talks and travels

Recent and upcoming

Older


PhD thesis