Bayesian DNN speeds up brain MRI segmentation.
problem Efficiently predicting brain MRI segmentations.
method Bayesian deep neural network with spike-and-slab dropout.
result Bayesian DNN outperforms existing methods in segmentation accuracy.
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Few-shot brain segmentation from weakly labeled data with deep heteroscedastic multi-task networkscs.LG
Few-shot brain segmentation achieved with weak labels and deep networks.
problem Efficient brain segmentation from limited labeled data.
method Heteroscedastic multi-task networks with Monte-Carlo inference and direct probability learning.
result Significant improvements in segmentation accuracy with minimal labeled data.
Machine Learning (ML) is increasingly being used for computer aided diagnosis of brain related disorders based on structural magnetic resonance imaging (MRI) data. Most of such work employs biologically and medically meaningful hand-crafted features calculated from different regions of the brain. The construction of su…
Inter-subject registration of cortical areas is necessary in functional imaging (fMRI) studies for making inferences about equivalent brain function across a population. However, many high-level visual brain areas are defined as peaks of functional contrasts whose cortical position is highly variable. As such, most ali…
Unified normative modeling for neuroimaging phenotypes using denoising diffusion models.
problem Discarding multivariate dependence in neuroimaging pipelines.
method Denoising diffusion probabilistic models (DDPMs) with FiLM and SAINT backbones.
result Unified multivariate normative modeling with better calibration and dependence preservation.
AutoML library TPOT optimizes brain age prediction models without prior knowledge.
problem Optimizing machine learning models for brain age prediction.
method TPOT uses genetic programming to find optimal machine learning pipelines.
result TPOT outperformed state-of-the-art models in brain age prediction.
Automated Thalamic Nuclei Segmentation Using Multi-Planar Cascaded Convolutional Neural Networkseess.IV
Fast, accurate thalamus segmentation method for MS and ET.
problem Automated thalamic nuclei segmentation for neurological diseases.
method Cascaded multi-planar scheme with modified residual U-Net architecture.
result Statistically significant improvements in thalamus segmentation for MS and ET patients.