Search Results for author: Anders Eklund

Found 16 papers, 7 papers with code

NUQ: A Noise Metric for Diffusion MRI via Uncertainty Discrepancy Quantification

no code implementations3 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.


Inflation of test accuracy due to data leakage in deep learning-based classification of OCT images

1 code implementation21 Feb 2022 Iulian Emil Tampu, Anders Eklund, Neda Haj-Hosseini

In this study, the effect of improper dataset splitting on model evaluation is demonstrated for two classification tasks using two OCT open-access datasets extensively used in the literature, Kermany's ophthalmology dataset and AIIMS breast tissue dataset.


Evaluation of augmentation methods in classifying autism spectrum disorders from fMRI data with 3D convolutional neural networks

no code implementations20 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.

What is the best data augmentation for 3D brain tumor segmentation?

2 code implementations26 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.

Brain Tumor Segmentation Data Augmentation +1

Does anatomical contextual information improve 3D U-Net based brain tumor segmentation?

no code implementations26 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).

Anatomy Brain Tumor Segmentation +2

Synthesizing brain tumor images and annotations by combining progressive growing GAN and SPADE

no code implementations13 Sep 2020 Mehdi Foroozandeh, Anders Eklund

Training segmentation networks requires large annotated datasets, but manual annotation is time consuming and costly.

Vox2Vox: 3D-GAN for Brain Tumour Segmentation

3 code implementations19 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.

Tumor Segmentation

Feeding the zombies: Synthesizing brain volumes using a 3D progressive growing GAN

1 code implementation11 Dec 2019 Anders Eklund

Deep learning requires large datasets for training (convolutional) networks with millions of parameters.

Generating fMRI volumes from T1-weighted volumes using 3D CycleGAN

no code implementations19 Jul 2019 David Abramian, Anders Eklund

Registration between an fMRI volume and a T1-weighted volume is challenging, since fMRI volumes contain geometric distortions.

Spatial 3D Matérn priors for fast whole-brain fMRI analysis

no code implementations25 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

Graph Spectral Characterization of Brain Cortical Morphology

no code implementations19 Feb 2019 Sevil Maghsadhagh, Anders Eklund, Hamid Behjat

The human brain cortical layer has a convoluted morphology that is unique to each individual.

Characterization of Brain Cortical Morphology Using Localized Topology-Encoding Graphs

no code implementations17 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.

Refacing: reconstructing anonymized facial features using GANs

1 code implementation15 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.

Translation Unsupervised Image-To-Image Translation

Generating Diffusion MRI scalar maps from T1 weighted images using generative adversarial networks

1 code implementation5 Oct 2018 Xuan Gu, Hans Knutsson, Markus Nilsson, Anders Eklund

Diffusion magnetic resonance imaging (diffusion MRI) is a non-invasive microstructure assessment technique.

Generative Adversarial Networks for Image-to-Image Translation on Multi-Contrast MR Images - A Comparison of CycleGAN and UNIT

3 code implementations20 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.

Computed Tomography (CT) Image-to-Image Translation +2

Gaussian process regression can turn non-uniform and undersampled diffusion MRI data into diffusion spectrum imaging

no code implementations9 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.


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