Publications

2024

Unsupervised Detection of Fetal Brain Anomalies using Denoising Diffusion Models

MDS Olsen, J Ambsdorf, M Lin, C Taksøe-Vester, MBS Svendsen, …
arXiv preprint arXiv:2408.03654

Unsupervised Detection of Fetal Brain Anomalies using Denoising Diffusion Models

M Ditlev Sjøgren Olsen, J Ambsdorf, M Lin, C Taksøe-Vester, …
arXiv e-prints, arXiv: 2408.03654

AMAES: Augmented Masked Autoencoder Pretraining on Public Brain MRI Data for 3D-Native Segmentation

A Munk, J Ambsdorf, S Llambias, M Nielsen
arXiv preprint arXiv:2408.00640

Yucca: A Deep Learning Framework For Medical Image Analysis

SN Llambias, J Machnio, A Munk, J Ambsdorf, M Nielsen, MM Ghazi
arXiv preprint arXiv:2407.19888

Introduction of one-view tomosynthesis in population-based mammography screening: Impact on detection rate, interval cancer rate and false-positive rate

BM Vilmun, G Napolitano, M Lillholm, RR Winkel, E Lynge, M Nielsen, …
Journal of Medical Screening, 09691413241262259

An assessment of the value of deep neural networks in genetic risk prediction for surgically relevant outcomes

MA Christensen, A Sigurdsson, A Bonde, S Rasmussen, SR Ostrowski, …
Plos one 19 (7), e0294368

Yucca: A Deep Learning Framework For Medical Image Analysis

S Nørgaard Llambias, J Machnio, A Munk, J Ambsdorf, M Nielsen, …
arXiv e-prints, arXiv: 2407.19888

Role of artificial-intelligence-assisted automated cardiac biometrics in prenatal screening for coarctation of aorta

CA Taksøe-Vester, K Mikolaj, OBB Petersen, NG Vejlstrup, …
Ultrasound in obstetrics & gynecology: the official journal of the …

Artificial intelligence for MRI stroke detection: a systematic review and meta-analysis

JA Bojsen, MT Elhakim, O Graumann, D Gaist, M Nielsen, FSG Harbo, …
Insights into Imaging 15 (1), 160

Early indicators of the impact of using AI in mammography screening for breast cancer

AD Lauritzen, M Lillholm, E Lynge, M Nielsen, N Karssemeijer, I Vejborg
Radiology 311 (3), e232479

Population-wide evaluation of artificial intelligence and radiologist assessment of screening mammograms

J Kühl, MT Elhakim, SW Stougaard, BSB Rasmussen, M Nielsen, O Gerke, …
European Radiology 34 (6), 3935-3946

CORE-BEHRT: A Carefully Optimized and Rigorously Evaluated BEHRT

M Odgaard, KV Klein, SM Thysen, E Jimenez-Solem, M Sillesen, …
arXiv preprint arXiv:2404.15201

AI supported fetal echocardiography with quality assessment

CA Taksoee-Vester, K Mikolaj, Z Bashir, AN Christensen, OB Petersen, …
Scientific Reports 14 (1), 5809

Integration of artificial intelligence (AI) in double-read population-based mammography screening: simulated replacement of one reader and beyond

MT Elhakim, SW Stougaard, O Graumann, M Nielsen, O Gerke, …\

Comparative analysis of multimodal biomarkers for amyloid-beta positivity detection in Alzheimer’s disease cohorts

M Mehdipour Ghazi, P Selnes, S Timón-Reina, S Tecelão, S Ingala, …
Frontiers in aging neuroscience 16, 1345417

Learning semantic image quality for fetal ultrasound from noisy ranking annotation

M Lin, J Ambsdorf, EPF Sejer, Z Bashir, CK Wong, P Pegios, A Raheli, …
arXiv preprint arXiv:2402.08294

Role of AI‐assisted automated cardiac biometrics in screening for fetal coarctation of aorta

CA Taksoee‐Vester, K Mikolaj, OBB Petersen, NG Vejlstrup, …
Ultrasound in Obstetrics & Gynecology

MDD-UNet: Domain Adaptation for Medical Image Segmentation with Theoretical Guarantees, a Proof of Concept

A Munk, A Ma, M Nielsen
Northern Lights Deep Learning Conference, 174-180

Heterogeneous Learning for Brain Lesion Segmentation, Detection, and Classification

SN Llambias, M Nielsen, MM Ghazi
Northern Lights Deep Learning Conference 2024

Local Gamma Augmentation for Ischemic Stroke Lesion Segmentation on MRI

J Middleton, M Bauer, K Sheng, J Johansen, M Perslev, S Ingala, …
Northern Lights Deep Learning Conference, 158-164

BRAIN COMMUNICATIONS

MV Sagar, NR Ferrer, MM Ghazi, KV Klein, E Jimenez-Solem, M Nielsen, …\

HyperLeaf2024-A Hyperspectral Imaging Dataset for Classification and Regression of Wheat Leaves

WM Laprade, P Pieta, S Kutuzova, JC Westergaard, M Nielsen, …
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …

2023

COVID-19-associated cerebral microbleeds in the general population

MV Sagar, NR Ferrer, M Mehdipour Ghazi, KV Klein, E Jimenez-Solem, …
Brain communications 6 (3), fcae127

Breast cancer detection accuracy of AI in an entire screening population: a retrospective, multicentre study

MT Elhakim, SW Stougaard, O Graumann, M Nielsen, K Lång, O Gerke, …
Cancer Imaging 23 (1), 127

Diagnostic test accuracy study of a commercially available deep learning algorithm for ischemic lesion detection on brain MRIs in suspected stroke patients from a non …

CH Krag, FC Müller, KL Gandrup, H Raaschou, MB Andersen, …
European Journal of Radiology 168, 111126

Active Transfer Learning for 3D Hippocampus Segmentation

J Wu, Z Kang, SN Llambias, MM Ghazi, M Nielsen
Workshop on Medical Image Learning with Limited and Noisy Data, 224-234

Leveraging Shape and Spatial Information for Spontaneous Preterm Birth Prediction

P Pegios, EPF Sejer, M Lin, Z Bashir, MBS Svendsen, M Nielsen, …
International Workshop on Advances in Simplifying Medical Ultrasound, 57-67

Can AI replace first reader in double reading? A large-scale, Danish multicentre study

MT Elhakim, SW Stougaard, O Graumann, O Gerke, M Nielsen, LB Larsen, …\

How accurate is AI for cancer detection in an entire mammography screening population?

MT Elhakim, JL Kühl, SW Stougaard, BS Rasmussen, O Gerke, M Nielsen, …\

Robust cross-vendor mammographic texture models using augmentation-based domain adaptation for long-term breast cancer risk

AD Lauritzen, MC von Euler-Chelpin, E Lynge, I Vejborg, M Nielsen, …
Journal of Medical Imaging 10 (5), 054003-054003

Assessing breast cancer risk by combining AI for lesion detection and mammographic texture

AD Lauritzen, MC von Euler-Chelpin, E Lynge, I Vejborg, M Nielsen, …
Radiology 308 (2), e230227

An improved medical scan protocol for in-scanner patient data acquisition analysis

M Nielsen, RM Lauritzen, ASU Pai
US Patent App. 17/621,668

Data Augmentation-Based Unsupervised Domain Adaptation In Medical Imaging

SN Llambias, M Nielsen, MM Ghazi
arXiv preprint arXiv:2308.04395

An Analysis of Spatial-Spectral Dependence in Hyperspectral Autoencoders

WM Laprade, JC Westergaard, J Nielsen, M Nielsen, AB Dahl
Scandinavian Conference on Image Analysis, 191-202

Deep learning-based assessment of cerebral microbleeds in COVID-19

NR Ferrer, MV Sagar, KV Klein, C Kruuse, M Nielsen, MM Ghazi
2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), 1-4

Breast density and risk of breast cancer

E Lynge, I Vejborg, M Lillholm, M Nielsen, G Napolitano, …
International Journal of Cancer 152 (6), 1150-1158

COVID-19 Associated Cerebral Microbleeds in the General Population

NR Ferrer, MV Sagar, MM Ghazi, KV Klein, E Jimenez-Solem, M Nielsen, …
Available at SSRN 4375623

Abstract P4-03-10: BREAST DENSITY AND RISK OF BREAST CANCER

E Lynge, I Vejborg, M Lillholm, M Nielsen, G Napolitano, …
Cancer Research 83 (5_Supplement), P4-03-10-P4-03-10

Online transfer learning with partial feedback

Z Kang, M Nielsen, B Yang, L Deng, SS Lorenzen
Expert Systems with Applications 212, 118738

Taxometer: Improving taxonomic classification of metagenomics contigs

S Kutuzova, M Nielsen, P Piera Lindez, J Nybo Nissen, S Rasmussen
bioRxiv, 2023.11. 23.568413

2022

Robust Identification of White Matter Hyperintensities in Uncontrolled Settings Using Deep Learning

A Schiavone, SN Llambias, J Johansen, S Ingala, A Pai, M Nielsen, …
Medical Imaging with Deep Learning, short paper track

Partial feedback online transfer learning with multi-source domains

Z Kang, M Nielsen, B Yang, MM Ghazi
Information Fusion 89, 29-40

Machine learning and deep learning applications in microbiome research

R Hernández Medina, S Kutuzova, KN Nielsen, J Johansen, LH Hansen, …
ISME communications 2 (1), 98

Instance-Specific Augmentation of Brain MRIs with Variational Autoencoders

J Middleton, M Bauer, J Johansen, M Nielsen, S Sommer, A Pai
MICCAI Workshop on Medical Applications with Disentanglements, 49-58

FAST-AID Brain: Fast and accurate segmentation tool using artificial intelligence developed for brain

MM Ghazi, M Nielsen
arXiv preprint arXiv:2208.14360

An artificial intelligence–based mammography screening protocol for breast cancer: outcome and radiologist workload

AD Lauritzen, A Rodríguez-Ruiz, MC von Euler-Chelpin, E Lynge, …
Radiology 304 (1), 41-49

Semantic Segmentation Techniques-Applications and Challenges: Investigating semantic segmentation techniques for pixel-level labeling of objects and scenes in images and videos

M Nielsen
Australian Journal of Machine Learning Research & Applications 2 (2), 108-117

CARRNN: A Continuous Autoregressive Recurrent Neural Network for Deep Representation Learning From Sporadic Temporal Data

MM Ghazi, L Sørensen, S Ourselin, M Nielsen
IEEE Transactions on Neural Networks and Learning Systems 35 (1), 792-802

2021

A buffered online transfer learning algorithm with multi-layer network

Z Kang, B Yang, M Nielsen, L Deng, S Yang
Neurocomputing 488, 581-597

Extraction of a bias field invariant biomarker from an image

L Sørensen, M Nielsen, C Igel
US Patent 11,341,691

Augmentation based unsupervised domain adaptation

M Orbes-Arteaga, T Varsavsky, L Sorensen, M Nielsen, A Pai, S Ourselin, …
arXiv preprint arXiv:2202.11486

Using machine learning for predicting intensive care unit resource use during the COVID-19 pandemic in Denmark

SS Lorenzen, M Nielsen, E Jimenez-Solem, TS Petersen, A Perner, …
Scientific reports 11 (1), 18959

S Kutuzova, C Igel, M Nielsen, D McCloskey
bioRxiv, 2021.08. 03.454944

Information bottleneck: Exact analysis of (quantized) neural networks

SS Lorenzen, C Igel, M Nielsen
arXiv preprint arXiv:2106.12912

Information Processing in Medical Imaging: 27th International Conference, IPMI 2021, Virtual Event, June 28–June 30, 2021, Proceedings

A Feragen, S Sommer, J Schnabel, M Nielsen
Springer Nature

Extension of plant phenotypes by the foliar microbiome

CV Hawkes, R Kjøller, JM Raaijmakers, L Riber, S Christensen, …
Annual Review of Plant Biology 72 (1), 823-846

2020

Lesion-wise evaluation for effective performance monitoring of small object segmentation

I Groothuis, CH Sudre, S Ingala, J Barnes, JD Gispert, L Sørensen, A Pai, …
Medical Imaging 2021: Image Processing 11596, 35-42

Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients

E Jimenez-Solem, TS Petersen, C Hansen, C Hansen, C Lioma, C Igel, …
Scientific reports 11 (1), 3246

Multimodal variational autoencoders for semi-supervised learning: In defense of product-of-experts

S Kutuzova, O Krause, D McCloskey, M Nielsen, C Igel
arXiv preprint arXiv:2101.07240

Robust parametric modeling of Alzheimer’s disease progression

MM Ghazi, M Nielsen, A Pai, M Modat, MJ Cardoso, S Ourselin, …
Neuroimage 225, 117460

Disease progression modeling‐based prediction of cognitive decline: Neuroimaging/Optimal neuroimaging measures for tracking disease progression

MM Ghazi, L Sørensen, A Pai, J Cardoso, M Modat, S Ourselin, M Nielsen
Alzheimer’s & Dementia 16, e043850

Position paper on COVID-19 imaging and AI: From the clinical needs and technological challenges to initial AI solutions at the lab and national level towards a new era for AI …

H Greenspan, RSJ Estépar, WJ Niessen, E Siegel, M Nielsen
Medical image analysis 66, 101800

Kunstig intelligens til cancerdiagnostik i brystkraeftscreening

MT Elhakim, O Graumann, LB Larsen, M Nielsen, BS Rasmussen
Ugeskr Laeger 182 (16), 1488-92

Artificial intelligence for cancer detection in breast cancer screening

MT Elhakim, O Graumann, LB Larsen, M Nielsen, BS Rasmussen
Ugeskrift for Laeger 182 (34), V06200423-V06200423

Disease Progression Modeling-Based Prediction of Cognitive Decline

MM Ghazi, L Sørensen, A Pai, J Cardoso, M Modat, S Ourselin, M Nielsen
2020 Alzheimer’s Association International Conference

2019

Inflammatory pathway analytes predicting rapid cognitive decline in MCI stage of Alzheimer’s disease

JA Pillai, J Bena, G Bebek, LM Bekris, A Bonner‐Jackson, L Kou, A Pai, …
Annals of Clinical and Translational Neurology 7 (7), 1225-1239

Impact of adding breast density to breast cancer risk models: a systematic review

BM Vilmun, I Vejborg, E Lynge, M Lillholm, M Nielsen, MB Nielsen, …
European journal of radiology 127, 109019

Chronic obstructive pulmonary disease quantification using CT texture analysis and densitometry: results from the Danish lung cancer screening trial

L Sørensen, M Nielsen, J Petersen, JH Pedersen, A Dirksen, M de Bruijne
American Journal of Roentgenology 214 (6), 1269-1279

Lung segmentation from chest X-rays using variational data imputation

R Selvan, EB Dam, NS Detlefsen, S Rischel, K Sheng, M Nielsen, A Pai
arXiv preprint arXiv:2005.10052

The alzheimer’s disease prediction of longitudinal evolution (TADPOLE) challenge: Results after 1 year follow-up

RV Marinescu, NP Oxtoby, AL Young, EE Bron, AW Toga, MW Weiner, …
arXiv preprint arXiv:2002.03419

Measurement challenge: protocol for international case–control comparison of mammographic measures that predict breast cancer risk

E Dench, D Bond-Smith, E Darcey, G Lee, YK Aung, A Chan, J Cuzick, …
BMJ open 9 (12), e031041

Sensitivity of screening mammography by density and texture: a cohort study from a population-based screening program in Denmark

M von Euler-Chelpin, M Lillholm, I Vejborg, M Nielsen, E Lynge
Breast Cancer Research 21, 1-7

Change in mammographic density across birth cohorts of Dutch breast cancer screening participants

G Napolitano, E Lynge, M Lillholm, I Vejborg, CH van Gils, M Nielsen, …
International Journal of Cancer 145 (11), 2954-2962

Multimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy: 4th International Workshop, MBIA 2019, and 7th International Workshop, MFCA 2019, Held in …

D Zhu, J Yan, H Huang, L Shen, PM Thompson, CF Westin, X Pennec, …
Springer Nature

The value of hippocampal volume, shape, and texture for 11-year prediction of dementia: a population-based study

HC Achterberg, L Sørensen, FJ Wolters, WJ Niessen, MW Vernooij, …
Neurobiology of aging 81, 58-66

Knowledge distillation for semi-supervised domain adaptation

M Orbes-Arteaga, J Cardoso, L Sørensen, C Igel, S Ourselin, M Modat, …
arXiv preprint arXiv:1908.07355

P4‐326: UNSUPERVISED MACHINE LEARNING ON BASELINE BRAIN MRI IDENTIFIES MCI SUBGROUP WITH A FASTER DECLINE OVER TWO YEARS COMPARED TO CLASSICAL HIPPOCAMPAL SPARING AD SUBTYPE

L Sørensen, A Pai, M Nielsen, JB Leverenz, JA Pillai
Alzheimer’s & Dementia 15, P1420-P1420

Bias correction in images

B Zou, ASU Pai, L Sørensen, M Nielsen
US Patent 10,332,241

Training recurrent neural networks robust to incomplete data: Application to Alzheimer’s disease progression modeling

MM Ghazi, M Nielsen, A Pai, MJ Cardoso, M Modat, S Ourselin, …
Medical image analysis 53, 39-46

2018

PADDIT: probabilistic augmentation of data using diffeomorphic image transformation

M Orbes-Arteaga, L Sørensen, J Cardoso, M Modat, S Ourselin, …
Medical Imaging 2019: Image Processing 10949, 197-202

On the initialization of long short-term memory networks

M Mehdipour Ghazi, M Nielsen, A Pai, M Modat, MJ Cardoso, S Ourselin, …
Neural Information Processing: 26th International Conference, ICONIP 2019 …

Multi-domain adaptation in brain MRI through paired consistency and adversarial learning

M Orbes-Arteaga, T Varsavsky, CH Sudre, Z Eaton-Rosen, LJ Haddow, …
Domain Adaptation and Representation Transfer and Medical Image Learning …

Knowledge distillation for semi-supervised domain adaptation

M Orbes-Arteainst, J Cardoso, L Sørensen, C Igel, S Ourselin, M Modat, …
OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical …

AI vil give store samfundsgevinster

PB Brockhoff, M Nielsen, J Damsgaard
Boersen, 5

Increasing accuracy of optimal surfaces using min-marginal energies

J Petersen, AM Arias-Lorza, R Selvan, D Bos, A van der Lugt, …
IEEE transactions on medical imaging 38 (7), 1559-1568

Subclinical depressive symptoms during late midlife and structural brain alterations: A longitudinal study of Danish men born in 1953

M Osler, L Sørensen, M Rozing, OP Calvo, M Nielsen, E Rostrup
Human Brain Mapping 39 (4), 1789-1795

Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics: First International Workshop, GRAIL 2017, 6th International Workshop, MFCA 2017, and Third …

MJ Cardoso, T Arbel, E Ferrante, X Pennec, AV Dalca, S Parisot, S Joshi, …
Springer

Computer analysis of mammograms

M Kallenberg, M Nielsen, M Lillholm
US Patent App. 15/054,839

Risk stratification of women with false-positive test results in mammography screening based on mammographic morphology and density: a case control study

RR Winkel, M von Euler-Chelpin, E Lynge, P Diao, M Lillholm, …
Cancer epidemiology 49, 53-60

[P4–225]: HIPPOCAMPAL TEXTURE PREDICTS RATE OF COGNITIVE DECLINE IN MILD COGNITIVE IMPAIRMENT

A Pai, JA Pillai, L Sørensen, S Darkner, S Sommer, M Nielsen, …
Alzheimer’s & Dementia 13 (7S_Part_28), P1356-P1356