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pro vyhledávání: '"Eklund, Anders"'
Large annotated datasets are required for training deep learning models, but in medical imaging data sharing is often complicated due to ethics, anonymization and data protection legislation. Generative AI models, such as generative adversarial netwo
Externí odkaz:
http://arxiv.org/abs/2306.02986
Diffusion models were initially developed for text-to-image generation and are now being utilized to generate high quality synthetic images. Preceded by GANs, diffusion models have shown impressive results using various evaluation metrics. However, c
Externí odkaz:
http://arxiv.org/abs/2305.07644
Using 3D CNNs on high resolution medical volumes is very computationally demanding, especially for large datasets like the UK Biobank which aims to scan 100,000 subjects. Here we demonstrate that using 2D CNNs on a few 2D projections (representing me
Externí odkaz:
http://arxiv.org/abs/2211.05762
Large annotated datasets are required to train segmentation networks. In medical imaging, it is often difficult, time consuming and expensive to create such datasets, and it may also be difficult to share these datasets with other researchers. Differ
Externí odkaz:
http://arxiv.org/abs/2211.04086
Diffusion MRI (dMRI) is the only non-invasive technique sensitive to tissue micro-architecture, which can, in turn, be used to reconstruct tissue microstructure and white matter pathways. The accuracy of such tasks is hampered by the low signal-to-no
Externí odkaz:
http://arxiv.org/abs/2203.01921
Publikováno v:
Sci Data 9, 580 (2022)
In the application of deep learning on optical coherence tomography (OCT) data, it is common to train classification networks using 2D images originating from volumetric data. Given the micrometer resolution of OCT systems, consecutive images are oft
Externí odkaz:
http://arxiv.org/abs/2202.12267
Graphics processing units (GPUs) are used today in a wide range of applications, mainly because they can dramatically accelerate parallel computing, are affordable and energy efficient. In the field of medical imaging, GPUs are in some cases crucial
Externí odkaz:
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-93673
Analysis of brain connectivity is important for understanding how information is processed by the brain. We propose a novel Bayesian vector autoregression (VAR) hierarchical model for analyzing brain connectivity in a resting-state fMRI data set with
Externí odkaz:
http://arxiv.org/abs/2112.04249
Classifying subjects as healthy or diseased using neuroimaging data has gained a lot of attention during the last 10 years. Here we apply deep learning to derivatives from resting state fMRI data, and investigate how different 3D augmentation techniq
Externí odkaz:
http://arxiv.org/abs/2110.10489
Publikováno v:
In Computers in Biology and Medicine January 2024 168