AIMC Topic: Brain

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A robust and interpretable deep learning framework for multi-modal registration via keypoints.

Medical image analysis
We present KeyMorph, a deep learning-based image registration framework that relies on automatically detecting corresponding keypoints. State-of-the-art deep learning methods for registration often are not robust to large misalignments, are not inter...

iHerd: an integrative hierarchical graph representation learning framework to quantify network changes and prioritize risk genes in disease.

PLoS computational biology
Different genes form complex networks within cells to carry out critical cellular functions, while network alterations in this process can potentially introduce downstream transcriptome perturbations and phenotypic variations. Therefore, developing e...

Short-axis PET image quality improvement based on a uEXPLORER total-body PET system through deep learning.

European journal of nuclear medicine and molecular imaging
PURPOSE: The axial field of view (AFOV) of a positron emission tomography (PET) scanner greatly affects the quality of PET images. Although a total-body PET scanner (uEXPLORER) with a large AFOV is more sensitive, it is more expensive and difficult t...

Deep learning for automated detection of generalized paroxysmal fast activity in Lennox-Gastaut syndrome.

Epilepsy & behavior : E&B
OBJECTIVES: Generalized paroxysmal fast activity (GPFA) is a key electroencephalographic (EEG) feature of Lennox-Gastaut Syndrome (LGS). Automated analysis of scalp EEG has been successful in detecting more typical abnormalities. Automatic detection ...

Neuromorphic Sentiment Analysis Using Spiking Neural Networks.

Sensors (Basel, Switzerland)
Over the past decade, the artificial neural networks domain has seen a considerable embracement of deep neural networks among many applications. However, deep neural networks are typically computationally complex and consume high power, hindering the...

Uncertainty-Aware Multi-Dimensional Mutual Learning for Brain and Brain Tumor Segmentation.

IEEE journal of biomedical and health informatics
Existing segmentation methods for brain MRI data usually leverage 3D CNNs on 3D volumes or employ 2D CNNs on 2D image slices. We discovered that while volume-based approaches well respect spatial relationships across slices, slice-based methods typic...

Fast deep learning reconstruction techniques for preclinical magnetic resonance fingerprinting.

NMR in biomedicine
We propose a deep learning (DL) model and a hyperparameter optimization strategy to reconstruct T and T maps acquired with the magnetic resonance fingerprinting (MRF) methodology. We applied two different MRF sequence routines to acquire images of ex...

Comparison of four synthetic CT generators for brain and prostate MR-only workflow in radiotherapy.

Radiation oncology (London, England)
BACKGROUND: The interest in MR-only workflows is growing with the introduction of artificial intelligence in the synthetic CT generators converting MR images into CT images. The aim of this study was to evaluate several commercially available sCT gen...

Improved prediction of behavioral and neural similarity spaces using pruned DNNs.

Neural networks : the official journal of the International Neural Network Society
Deep Neural Networks (DNNs) have become an important tool for modeling brain and behavior. One key area of interest has been to apply these networks to model human similarity judgements. Several previous works have used the embeddings from the penult...

Synchronization in STDP-driven memristive neural networks with time-varying topology.

Journal of biological physics
Synchronization is a widespread phenomenon in the brain. Despite numerous studies, the specific parameter configurations of the synaptic network structure and learning rules needed to achieve robust and enduring synchronization in neurons driven by s...