| 1. |
- Appleton, O, et al.
(författare)
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The next-generation ARC middleware
- 2010
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Ingår i: ANNALS OF TELECOMMUNICATIONS-ANNALES DES TELECOMMUNICATIONS. - : Presses Polytechniques Romandes. - 0003-4347 .- 1958-9395. ; 65:11-12, s. 771-776
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Tidskriftsartikel (refereegranskat)abstract
- The Advanced Resource Connector (ARC) is a light-weight, non-intrusive, simple yet powerful Grid middleware capable of connecting highly heterogeneous computing and storage resources. ARC aims at providing general purpose, flexible, collaborative computing environments suitable for a range of uses, both in science and business. The server side offers the fundamental job execution management, information and data capabilities required for a Grid. Users are provided with an easy to install and use client which provides a basic toolbox for job- and data management. The KnowARC project developed the next-generation ARC middleware, implemented as Web Services with the aim of standard-compliant interoperability.
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| 2. |
- Jönemo, Johan, 1974-, et al.
(författare)
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Brain Age Prediction Using 2D Projections Based on Higher-Order Statistical Moments and Eigenslices from 3D Magnetic Resonance Imaging Volumes
- 2023
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Ingår i: Journal of Imaging. - : MDPI. - 2313-433X. ; 9:12
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Tidskriftsartikel (refereegranskat)abstract
- Brain age prediction from 3D MRI volumes using deep learning has recently become a popular research topic, as brain age has been shown to be an important biomarker. Training deep networks can be very computationally demanding for large datasets like the U.K. Biobank (currently 29,035 subjects). In our previous work, it was demonstrated that using a few 2D projections (mean and standard deviation along three axes) instead of each full 3D volume leads to much faster training at the cost of a reduction in prediction accuracy. Here, we investigated if another set of 2D projections, based on higher-order statistical central moments and eigenslices, leads to a higher accuracy. Our results show that higher-order moments do not lead to a higher accuracy, but that eigenslices provide a small improvement. We also show that an ensemble of such models provides further improvement.
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| 3. |
- Jönemo, Johan, 1974-
(författare)
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Deep learning on large neuroimaging datasets
- 2024
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Magnetic resonance imaging (MRI) is a medical imaging method that has become increasingly more important during the last 4 decades. This is partly because it allows us to acquire a 3D-representation of a part of the body without exposing patients to ionizing radiation. Furthermore, it also typically gives better contrast between soft tissues than x-ray based techniques such as CT. The image acquisition procedure of MRI is also much more flexible. One can vary the signal sequence, not only to change how different types of tissue map to different intensities, but also to measure flow, diffusion or even brain activity over time. Machine learning has gained great impetus the last decade and a half. This is probably partly because of the work done on the mathematical foundations of machine learning done at the end of last century in conjunction with the availability of specialized massively parallel processors, originally developed as graphical processing units (GPUs), which are ideal for training or running machine learning models. The work presented in this thesis combines MRI and machine learning in order to leverage the large amounts of MRI-data available in open data sets, to address questions of clinical relevance about the brain. The thesis comprises three studies. In the first one the subproblem which augmentation methods are useful in the larger context of classifying autism, was investigated. The second study is about predicting brain age. In particular it aims to construct light-weight models using the MRI volumes in a condensed form, so that the model can be trained in a short time and still reach good accuracy. The third study is a development of the previous that investigates other ways of condensing the brain volumes.
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| 4. |
- Jönemo, Johan, 1974-, et al.
(författare)
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Efficient Brain Age Prediction from 3D MRI Volumes Using 2D Projections
- 2023
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Ingår i: Brain Sciences. - : MDPI. - 2076-3425. ; 13:9
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Tidskriftsartikel (refereegranskat)abstract
- Using 3D CNNs on high-resolution medical volumes is very computationally demanding, especially for large datasets like UK Biobank, which aims to scan 100,000 subjects. Here, we demonstrate that using 2D CNNs on a few 2D projections (representing mean and standard deviation across axial, sagittal and coronal slices) of 3D volumes leads to reasonable test accuracy (mean absolute error of about 3.5 years) when predicting age from brain volumes. Using our approach, one training epoch with 20,324 subjects takes 20–50 s using a single GPU, which is two orders of magnitude faster than a small 3D CNN. This speedup is explained by the fact that 3D brain volumes contain a lot of redundant information, which can be efficiently compressed using 2D projections. These results are important for researchers who do not have access to expensive GPU hardware for 3D CNNs.
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| 5. |
- Jönemo, Johan, 1974-, et al.
(författare)
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Evaluation of Augmentation Methods in Classifying Autism Spectrum Disorders from fMRI Data with 3D Convolutional Neural Networks
- 2023
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Ingår i: Diagnostics. - : MDPI. - 2075-4418. ; 13:17
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Tidskriftsartikel (refereegranskat)abstract
- Classifying subjects as healthy or diseased using neuroimaging data has gained a lot of attention during the last 10 years, and recently, different deep learning approaches have been used. Despite this fact, there has not been any investigation regarding how 3D augmentation can help to create larger datasets, required to train deep networks with millions of parameters. In this study, deep learning was applied to derivatives from resting state functional MRI data, to investigate how different 3D augmentation techniques affect the test accuracy. Specifically, resting state derivatives from 1112 subjects in ABIDE (Autism Brain Imaging Data Exchange) preprocessed were used to train a 3D convolutional neural network (CNN) to classify each subject according to presence or absence of autism spectrum disorder. The results show that augmentation only provide minor improvements to the test accuracy.
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