Research

Current research activities

My research focus is machine learning engineering, focusing on medical data analysis (Medical AI).

Most of my activities are related to the Mohn Medical Imaging and Visualization Centre at the Department of Radiology, Haukeland University Hospital, funded by the Trond Mohn Foundation. I am part of the center's leadership team and lead the Medical AI group.

At the Western Norway University of Applied Sciences (HVL) I am part of Computer Science: Software Engineering, Sensor Networks and Engineering Computing, the Health Informatics research group, the Data Science & AI group, and the multidisciplinary research group Idrett, Helse og Funksjon (Sports, health and function).

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

PhD supervision

Main supervisor

Ongoing
Completed

Co-supervision

Ongoing
Completed

Postdocs, main mentor

 

Completed

Publications

See my Google Scholar profile for an up-to-date list:

Functional activity level reported by an informant is an early predictor of Alzheimer's disease
Vik A, Kociński, M, Rye I, Lundervold AJ, Lundervold AS BMC Geriatrics (2023) 23:205
LAB-Net: Lidar and aerial image-based building segmentation using U-Nets
Kaliyugarasan S, Lundervold AS Nordic Machine Intelligence, Vol. 2 No. 3 (2023)
A probability transducer and decision-theoretic augmentation for machine-learning classifiers
Dyrland K, Lundervold AS, Porta Mana, PGL OSF Preprints, 2022
Predicting conversion to Alzheimer's Disease in individuals with Mild Cognitive Impairment using clinically transferable features
I. Rye, A. Vik, M. Kocinski, A.S. Lundervold, A.J. Lundervold Scientific Reports, 12, 2022
Brain Tumor Segmentation from Multiparametric MRI Using a Multi-encoder U-Net Architecture
S Alam, B Halandur, PGL Mana, D Goplen, A Lundervold, AS Lundervold BrainLes 2021. Lecture Notes in Computer Science, vol 12963
Fully Automatic Whole-Volume Tumor Segmentation in Cervical Cancer
Hodneland E, Kaliyugarasan S, Wagner-Larsen KS, Lura N, Andersen E, Bartsch H, Smit N, Halle MK, Krakstad C, Lundervold AS, Haldorsen IS Cancers. 2022; 14(10):2372
A predictive framework based on brain volume trajectories enabling early detection of Alzheimer's disease.
S.A. Mofrad, A. Lundervold and A.S. Lundervold Computerized Medical Imaging and Graphics, 2021
Automated segmentation of endometrial cancer on multimodal MR images using deep learning.
Hodneland, E., Dybvik, J.A., Wagner-Larsen, K.S., Solteszova, V., Munthe-Kaas, A.Z., Fasmer, K.E., Krakstad, C., Lundervold, A., Lundervold, A.S., Salvesen, Ø., Erickson, B.J., Haldorsen, I. Scientific Reports, 2021
Pulmonary Nodule Classification in Lung Cancer from 3D Thoracic CT Scans Using fastai and MONAI
S. Kaliyugarasan, A. Lundervold and A.S. Lundervold IJIMAI, 2021
Cognitive and MRI trajectories for prediction of Alzheimer’s disease.
S.A. Mofrad, A.J. Lundervold, A. Vik and A.S. Lundervold Scientific Reports, 2021
2D and 3D U-Nets for skull stripping in a large and heterogeneous set of head MRI using fastai.
Kaliyugarasan S, Kocinski M, Lundervold A, Lundervold AS. , NIK2020, no.1, 2020
Synthesizing skin lesion images using CycleGANs – a case study.
Fossen-Romsaas S, Storm-Johannessen A, Lundervold AS. , NIK2020, no.1, 2020
An overview of deep learning in medical imaging focusing on MRI
with A. Lundervold, Zeitschrift fuer Medizinische Physik, Volume 29, Issue 2, 2019
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
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

Talks

Recent and upcoming

Older

Recent research projects

More to be added

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

In 2015 I was co-PI in the project Computational medicine: Numerical models for medical images and signals, funded by UH-Nett Vest, with partners from HVL, UiB, HUS and UiS

PhD thesis