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06/05/2023
Brain tumor segmentation using synthetic MR images – A comparison of GANs and diffusion models
Large annotated datasets are required for training deep learning models,...
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05/12/2023
Beware of diffusion models for synthesizing medical images – A comparison with GANs in terms of memorizing brain tumor images
Diffusion models were initially developed for text-to-image generation a...
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11/10/2022
Efficient brain age prediction from 3D MRI volumes using 2D projections
Using 3D CNNs on high resolution medical volumes is very computationally...
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11/08/2022
Does an ensemble of GANs lead to better performance when training segmentation networks with synthetic images?
Large annotated datasets are required to train segmentation networks. In...
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03/03/2022
NUQ: A Noise Metric for Diffusion MRI via Uncertainty Discrepancy Quantification
Diffusion MRI (dMRI) is the only non-invasive technique sensitive to tis...
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02/21/2022
Inflation of test accuracy due to data leakage in deep learning-based classification of OCT images
In the application of deep learning on optical coherence tomography (OCT...
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12/08/2021
Bayesian Modeling of Effective and Functional Brain Connectivity using Hierarchical Vector Autoregressions
Analysis of brain connectivity is important for understanding how inform...
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10/20/2021
Evaluation of augmentation methods in classifying autism spectrum disorders from fMRI data with 3D convolutional neural networks
Classifying subjects as healthy or diseased using neuroimaging data has ...
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10/26/2020
Does contextual information improve 3D U-Net based brain tumor segmentation?
Effective, robust and automatic tools for brain tumor segmentation are n...
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10/26/2020
What is the best data augmentation approach for brain tumor segmentation using 3D U-Net?
Training segmentation networks requires large annotated datasets, which ...
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09/13/2020
Synthesizing brain tumor images and annotations by combining progressive growing GAN and SPADE
Training segmentation networks requires large annotated datasets, but ma...
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12/11/2019
Feeding the zombies: Synthesizing brain volumes using a 3D progressive growing GAN
Deep learning requires large datasets for training (convolutional) netwo...
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10/18/2019
Anatomically informed Bayesian spatial priors for fMRI analysis
Existing Bayesian spatial priors for functional magnetic resonance imagi...
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07/19/2019
Generating fMRI volumes from T1-weighted volumes using 3D CycleGAN
Registration between an fMRI volume and a T1-weighted volume is challeng...
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06/25/2019
Spatial 3D Matérn priors for fast whole-brain fMRI analysis
Bayesian whole-brain functional magnetic resonance imaging (fMRI) analys...
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02/19/2019
Graph Spectral Characterization of Brain Cortical Morphology
The human brain cortical layer has a convoluted morphology that is uniqu...
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10/17/2018
Characterization of Brain Cortical Morphology Using Localized Topology-Encoding Graphs
The human brain cortical layer has a convoluted morphology that is uniqu...
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10/15/2018
Refacing: reconstructing anonymized facial features using GANs
Anonymization of medical images is necessary for protecting the identity...
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10/05/2018
Generating Diffusion MRI scalar maps from T1 weighted images using generative adversarial networks
Diffusion magnetic resonance imaging (diffusion MRI) is a non-invasive m...
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10/05/2018
Reply to Chen et al.: Parametric methods for cluster inference perform worse for two-sided t-tests
One-sided t-tests are commonly used in the neuroimaging field, but two-s...
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06/20/2018
Generative Adversarial Networks for Image-to-Image Translation on Multi-Contrast MR Images - A Comparison of CycleGAN and UNIT
In medical imaging, a general problem is that it is costly and time cons...
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04/09/2018
Cluster Failure Revisited: Impact of First Level Design and Data Quality on Cluster False Positive Rates
Methodological research rarely generates a broad interest, yet our work ...
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11/16/2017
Bayesian uncertainty quantification in linear models for diffusion MRI
Diffusion MRI (dMRI) is a valuable tool in the assessment of tissue micr...
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11/09/2016