Håkan Ahlström
Orcid: 0000-0002-8701-969X
According to our database1,
Håkan Ahlström
authored at least 18 papers
between 2013 and 2024.
Collaborative distances:
Collaborative distances:
Timeline
Legend:
Book In proceedings Article PhD thesis Dataset OtherLinks
On csauthors.net:
Bibliography
2024
A method for supervoxel-wise association studies of age and other non-imaging variables from coronary computed tomography angiograms.
CoRR, 2024
Prediction of Total Metabolic Tumor Volume from Tissue-Wise FDG-PET/CT Projections, Interpreted Using Cohort Saliency Analysis.
Proceedings of the Medical Image Understanding and Analysis - 28th Annual Conference, 2024
2023
Automatic segmentation of large-scale CT image datasets for detailed body composition analysis.
BMC Bioinform., December, 2023
Introducing Spatial Context in Patch-Based Deep Learning for Semantic Segmentation in Whole Body MRI.
Proceedings of the Image Analysis - 22nd Scandinavian Conference, 2023
2021
Deep regression for uncertainty-aware and interpretable analysis of large-scale body MRI.
CoRR, 2021
Uncertainty-aware body composition analysis with deep regression ensembles on UK Biobank MRI.
Comput. Medical Imaging Graph., 2021
2020
Identifying Morphological Indicators of Aging With Neural Networks on Large-Scale Whole-Body MRI.
IEEE Trans. Medical Imaging, 2020
CoRR, 2020
Large-scale biometry with interpretable neural network regression on UK Biobank body MRI.
CoRR, 2020
Fast graph-cut based optimization for practical dense deformable registration of volume images.
Comput. Medical Imaging Graph., 2020
Proceedings of the SIET '20: 5th International Conference on Sustainable Information Engineering and Technology, 2020
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2020, 2020
2018
Separation of water and fat signal in whole-body gradient echo scans using convolutional neural networks.
CoRR, 2018
Fully Convolutional Networks for Automated Segmentation of Abdominal Adipose Tissue Depots in Multicenter Water-Fat MRI.
CoRR, 2018
2015
Medical Image Anal., 2015
2013
Intracranial volume estimated with commonly used methods could introduce bias in studies including brain volume measurements.
NeuroImage, 2013