5 citations · 16 across the 5 of their papers we have counts for
17 papers
NUQ: A Noise Metric for Diffusion MRI via Uncertainty Discrepancy Quantification
Shreyas Fadnavis, Jens Sjölund, Anders Eklund +1
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 pa…
Evaluation of augmentation methods in classifying autism spectrum disorders from fMRI data with 3D convolutional neural networks
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. Here we apply deep learning to derivatives from resting…
What is the best data augmentation for 3D brain tumor segmentation?
Marco Domenico Cirillo, David Abramian, Anders Eklund
Training segmentation networks requires large annotated datasets, which in medical imaging can be hard to obtain. Despite this fact, data augmentation has in our opinion not been f…
Synthesizing brain tumor images and annotations by combining progressive growing GAN and SPADE
Mehdi Foroozandeh, Anders Eklund
Training segmentation networks requires large annotated datasets, but manual annotation is time consuming and costly. We here investigate if the combination of a noise-to-image GAN…
Vox2Vox: 3D-GAN for Brain Tumour Segmentation
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., perit…
Feeding the zombies: Synthesizing brain volumes using a 3D progressive growing GAN
Anders Eklund
Deep learning requires large datasets for training (convolutional) networks with millions of parameters. In neuroimaging, there are few open datasets with more than 100 subjects, w…