Mads's works
Collaboratories
AI Connects
Mads's P1 Publications
Deep Learning for Localization of White Matter Lesions in Neurological Diseases
Julia Machnio, Mads Nielsen, Mostafa M. Ghazi
Assessing the Efficacy of Classical and Deep Neuroimaging Biomarkers in Early Alzheimer’s Disease Diagnosis
Milla E. Nielsen, Mads Nielsen, Mostafa M. Ghazi
AMAES: Augmented Masked Autoencoder Pretraining on Public Brain MRI Data for 3D-Native Segmentation
Asbjørn Munk, Jakob Ambsdorf, Sebastian Llambias, Mads Nielsen
Unsupervised Detection of Fetal Brain Anomalies Using Denoising Diffusion Models
Markus D. S. Olsen, Jakob Ambsdorf, Manxi Lin, Caroline Taksøe-Vester, Morten B. S. Svendsen, Anders N. Christensen, Mads Nielsen, Martin G. Tolsgaard, Aasa Feragen, Paraskevas Pegios
Taxometer: Improving taxonomic classification of metagenomics contigs
Svetlana Kutuzova, Mads Nielsen, Pau Piera, Jakob N. Nissen, Simon Rasmussen
Role of artificial-intelligence-assisted automated cardiac biometrics in prenatal screening for coarctation of aorta
Caroline A. Taksøe-Vester, Kamil Mikolaj, Olav B. B. Petersen, Niels G. Vejlstrup, Anders N. Christensen, Aasa Feragen, Mads Nielsen, Morten B. S. Svendsen, Martin G. Tolsgaard
MDD-UNet: Domain Adaptation for Medical Image Segmentation with Theoretical Guarantees, a Proof of Concept
Asbjørn Munk, Ao Ma, Mads Nielsen
Local Gamma Augmentation for Ischemic Stroke Lesion Segmentation on MRI
Jon Middleton, Marko Bauer, Kaining Sheng, Jacob Johansen, Mathias Perslev, Silvia Ingala, Mads Nielsen, Akshay Pai
Learning Semantic Image Quality for Fetal Ultrasound from Noisy Ranking Annotation
Manxi Lin, Jakob Ambsdorf, Emilie P. F. Sejer, Zahra Bashir, Chun K. Wong, Paraskevas Pegios, Alberto Raheli, Morten B. S. Svendsen, Mads Nielsen, Martin G. Tolsgaard, Anders N. Christensen, Aasa Feragen
HyperLeaf2024 – A Hyperspectral Imaging Dataset for Classification and Regression of Wheat Leaves
William M. Laprade, Pawel Pieta, Svetlana Kutuzova, Jesper C. Westergaard, Mads Nielsen, Svend Christensen, Anders B. Dahl
Heterogeneous Learning for Brain Lesion Segmentation, Detection, and Classification
Sebastian N. Llambias, Mads Nielsen, Mostafa M. Ghazi
Early Indicators of the Impact of Using AI in Mammography Screening for Breast Cancer
Andreas D. Lauritzen, Martin Lillholm, Elsebeth Lynge, Mads Nielsen, Nico Karssemeijer, Ilse Vejborg
COVID-19 Associated Cerebral Microbleeds in the General Population
Malini V. Sagar, Neus R. Ferrer, Mostafa M. Ghazi, Kiril V. Klein, Espen Jimenez-Solem, Mads Nielsen, Christina Kruuse
CORE-BEHRT: A Carefully Optimized and Rigorously Evaluated BEHRT
Mikkel Odgaard, Kiril V. Klein, Sanne M. Thysen, Espen Jimenez-Solem, Martin Sillesen, Mads Nielsen
Comparative analysis of multimodal biomarkers for amyloid-beta positivity detection in Alzheimer’s disease cohorts
Mostafa M. Ghazi, Per Selnes, Santiago Timón-Reina, Sandra Tecelão, Silvia Ingala, Atle Bjørnerud, Bjørn-Eivind Kirsebom, Tormod Fladby, Mads Nielsen
Cognitive aging and reserve factors in the Metropolit 1953 Danish male cohort
Mostafa M. Ghazi, Olalla Urdanibia-Centelles, Aftab Bakhtiari, Birgitte Fagerlund, Mark B. Vestergaard, Henrik B. W. Larsson, Erik L. Mortensen, Merete Osler, Mads Nielsen, Krisztina Benedek, Martin Lauritzen
AI supported fetal echocardiography with quality assessment
Caroline A. Taksoee-Vester, Kamil Mikolaj, Zahra Bashir, Anders N. Christensen, Olav B. Petersen, Karin Sundberg, Aasa Feragen, Morten B. S. Svendsen, Mads Nielsen, Martin G. Tolsgaard
Robust Identification of White Matter Hyperintensities in Uncontrolled Settings Using Deep Learning
Alice Schiavone, Sebastian N. Llambias, Jacob Johansen, Silvia Ingala, Akshay Pai, Mads Nielsen, Mostafa M. Ghazi
Partial feedback online transfer learning with multi-source domains
Zhongfeng Kang, Mads Nielsen, Bo Yang, Mostafa M. Ghazi
Deep Learning-Based Assessment of Cerebral Microbleeds in COVID-19
Neus R. Ferrer, Malini V. Sagar, Kiril V. Klein, Christina Kruuse, Mads Nielsen, Mostafa M. Ghazi
Assessing Breast Cancer Risk by Combining AI for Lesion Detection and Mammographic Texture
Andreas D. Lauritzen, My C. von Euler-Chelpin, Elsebeth Lynge, Ilse Vejborg, Mads Nielsen, Nico Karssemeijer, Martin Lillholm
Active Transfer Learning for 3D Hippocampus Segmentation
Ji Wu, Zhongfeng Kang, Sebastian N. Llambias, Mostafa M. Ghazi, Mads Nielsen
Leveraging Shape and Spatial Information for Spontaneous Preterm Birth Prediction
Paraskevas Pegios, Emilie P. F. Sejer, Manxi Lin, Zahra Bashir, Morten B. S. Svendsen, Mads Nielsen, Eike Petersen, Anders N. Christensen, Martin Tolsgaard, Aasa Feragen
Online transfer learning with partial feedback
Zhongfeng Kang, Mads Nielsen, Bo Yang, Lihui Deng, Stephan S. Lorenzen
Robust cross-vendor mammographic texture models using augmentation-based domain adaptation for long-term breast cancer risk
Andreas D. Lauritzen, My C. von Euler-Chelpin, Elsebeth Lynge, Ilse Vejborg, Mads Nielsen, Nico Karssemeijer, Martin Lillholm
An Artificial Intelligence–based Mammography Screening Protocol for Breast Cancer: Outcome and Radiologist Workload
Andreas D. Lauritzen, Alejandro Rodríguez-Ruiz, My C. von Euler-Chelpin, Elsebeth Lynge, Ilse Vejborg, Mads Nielsen, Nico Karssemeijer, Martin Lillholm
CARRNN: A Continuous Autoregressive Recurrent Neural Network for Deep Representation Learning From Sporadic Temporal Data
Mostafa M. Ghazi, Lauge Sørensen, Sébastien Ourselin, Mads Nielsen
Machine learning and deep learning applications in microbiome research
Ricardo H. Medina, Svetlana Kutuzova, Knud N. Nielsen, Joachim Johansen, Lars H. Hansen, Mads Nielsen, Simon Rasmussen
A buffered online transfer learning algorithm with multi-layer network
Zhongfeng Kang, Bo Yang, Mads Nielsen, Lihui Deng, Shantian Yang