1 code implementation • 5 Jun 2023 • Muhammad Usman Akbar, Måns Larsson, Anders Eklund
Our results show that segmentation networks trained on synthetic images reach Dice scores that are 80% - 90% of Dice scores when training with real images, but that memorization of the training images can be a problem for diffusion models if the original dataset is too small.
no code implementations • 12 May 2023 • Muhammad Usman Akbar, Wuhao Wang, Anders Eklund
Diffusion models were initially developed for text-to-image generation and are now being utilized to generate high-quality synthetic images.
1 code implementation • 10 Nov 2022 • Johan Jönemo, Muhammad Usman Akbar, Robin Kämpe, J Paul Hamilton, Anders Eklund
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.
no code implementations • 8 Nov 2022 • Måns Larsson, Muhammad Usman Akbar, Anders Eklund
Large annotated datasets are required to train segmentation networks.
no code implementations • 3 Mar 2022 • Shreyas Fadnavis, Jens Sjölund, Anders Eklund, Eleftherios Garyfallidis
However, it is hard to estimate the impact of noise on downstream tasks based only on such qualitative assessments.
1 code implementation • 21 Feb 2022 • Iulian Emil Tampu, Anders Eklund, Neda Haj-Hosseini
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.
no code implementations • 20 Oct 2021 • Johan Jönemo, David Abramian, Anders Eklund
Classifying subjects as healthy or diseased using neuroimaging data has gained a lot of attention during the last 10 years.
no code implementations • 26 Oct 2020 • Iulian Emil Tampu, Neda Haj-Hosseini, Anders Eklund
The BraTS2020 dataset was used to train and test two standard 3D U-Net models that, in addition to the conventional MR image modalities, used the anatomical contextual information as extra channels in the form of binary masks (CIM) or probability maps (CIP).
2 code implementations • 26 Oct 2020 • Marco Domenico Cirillo, David Abramian, Anders Eklund
Training segmentation networks requires large annotated datasets, which in medical imaging can be hard to obtain.
no code implementations • 13 Sep 2020 • Mehdi Foroozandeh, Anders Eklund
Training segmentation networks requires large annotated datasets, but manual annotation is time consuming and costly.
3 code implementations • 19 Mar 2020 • Marco Domenico Cirillo, David Abramian, Anders Eklund
Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histological sub-regions, i. e., peritumoral edema, necrotic core, enhancing and non-enhancing tumour core.
1 code implementation • 11 Dec 2019 • Anders Eklund
Deep learning requires large datasets for training (convolutional) networks with millions of parameters.
no code implementations • 19 Jul 2019 • David Abramian, Anders Eklund
Registration between an fMRI volume and a T1-weighted volume is challenging, since fMRI volumes contain geometric distortions.
no code implementations • 25 Jun 2019 • Per Sidén, Finn Lindgren, David Bolin, Anders Eklund, Mattias Villani
Bayesian whole-brain functional magnetic resonance imaging (fMRI) analysis with three-dimensional spatial smoothing priors have been shown to produce state-of-the-art activity maps without pre-smoothing the data.
Methodology Applications Computation
no code implementations • 19 Feb 2019 • Sevil Maghsadhagh, Anders Eklund, Hamid Behjat
The human brain cortical layer has a convoluted morphology that is unique to each individual.
no code implementations • 17 Oct 2018 • Sevil Maghsadhagh, Mousa Shamsi, Anders Eklund, Hamid Behjat
Spectral features of these graphs are then studied and proposed as descriptors of cortical morphology.
1 code implementation • 15 Oct 2018 • David Abramian, Anders Eklund
Anonymization of medical images is necessary for protecting the identity of the test subjects, and is therefore an essential step in data sharing.
1 code implementation • 5 Oct 2018 • Xuan Gu, Hans Knutsson, Markus Nilsson, Anders Eklund
Diffusion magnetic resonance imaging (diffusion MRI) is a non-invasive microstructure assessment technique.
3 code implementations • 20 Jun 2018 • Per Welander, Simon Karlsson, Anders Eklund
Here, we evaluate two unsupervised GAN models (CycleGAN and UNIT) for image-to-image translation of T1- and T2-weighted MR images, by comparing generated synthetic MR images to ground truth images.
no code implementations • 9 Nov 2016 • Jens Sjölund, Anders Eklund, Evren Özarslan, Hans Knutsson
We propose to use Gaussian process regression to accurately estimate the diffusion MRI signal at arbitrary locations in q-space.