AIMC Topic: Neural Networks, Computer

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Extreme neural machines.

Neural networks : the official journal of the International Neural Network Society
Recurrent neural networks can solve a variety of computational tasks and produce patterns of activity that capture key properties of brain circuits. However, learning rules designed to train these models are time-consuming and prone to inaccuracies w...

Predicting the retinotopic organization of human visual cortex from anatomy using geometric deep learning.

NeuroImage
Whether it be in a single neuron or a more complex biological system like the human brain, form and function are often directly related. The functional organization of human visual cortex, for instance, is tightly coupled with the underlying anatomy ...

CEREBRUM-7T: Fast and Fully Volumetric Brain Segmentation of 7 Tesla MR Volumes.

Human brain mapping
Ultra-high-field magnetic resonance imaging (MRI) enables sub-millimetre resolution imaging of the human brain, allowing the study of functional circuits of cortical layers at the meso-scale. An essential step in many functional and structural neuroi...

Real-time frequency-independent single-Lead and single-beat myocardial infarction detection.

Artificial intelligence in medicine
This study proposes a novel real-time frequency-independent myocardial infarction detector for Lead II electrocardiograms. The underlying Deep-LSTM network is trained using the PTB-XL database, the largest to date publicly available electrocardiograp...

Deep learning based microscopic cell images classification framework using multi-level ensemble.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Advancement of the ultra-fast microscopic images acquisition and generation techniques give rise to the automated artificial intelligence (AI)-based microscopic images classification systems. The earlier cell classification...

Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features.

IEEE transactions on pattern analysis and machine intelligence
This work presents a novel method of exploring human brain-visual representations, with a view towards replicating these processes in machines. The core idea is to learn plausible computational and biological representations by correlating human neur...

HybridCTrm: Bridging CNN and Transformer for Multimodal Brain Image Segmentation.

Journal of healthcare engineering
Multimodal medical image segmentation is always a critical problem in medical image segmentation. Traditional deep learning methods utilize fully CNNs for encoding given images, thus leading to deficiency of long-range dependencies and bad generaliza...

Localizing category-related information in speech with multi-scale analyses.

PloS one
Measurements of the physical outputs of speech-vocal tract geometry and acoustic energy-are high-dimensional, but linguistic theories posit a low-dimensional set of categories such as phonemes and phrase types. How can it be determined when and where...

Machine learning techniques demonstrating individual movement patterns of the vertebral column: the fingerprint of spinal motion.

Computer methods in biomechanics and biomedical engineering
Surface topography systems enable the capture of spinal dynamic movement; however, it is unclear whether vertebral dynamics are unique enough to identify individuals. Therefore, in this study, we investigated whether the identification of individuals...

Weak sub-network pruning for strong and efficient neural networks.

Neural networks : the official journal of the International Neural Network Society
Pruning methods to compress and accelerate deep convolutional neural networks (CNNs) have recently attracted growing attention, with the view of deploying pruned networks on resource-constrained hardware devices. However, most existing methods focus ...