AIMC Topic: Deep Learning

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MCCA-VNet: A Vit-Based Deep Learning Approach for Micro-Expression Recognition Based on Facial Coding.

Sensors (Basel, Switzerland)
In terms of facial expressions, micro-expressions are more realistic than macro-expressions and provide more valuable information, which can be widely used in psychological counseling and clinical diagnosis. In the past few years, deep learning metho...

Virtual histopathology methods in medical imaging - a systematic review.

BMC medical imaging
Virtual histopathology is an emerging technology in medical imaging that utilizes advanced computational methods to analyze tissue images for more precise disease diagnosis. Traditionally, histopathology relies on manual techniques and expertise, oft...

Deep learning model for the automated detection and classification of central canal and neural foraminal stenosis upon cervical spine magnetic resonance imaging.

BMC medical imaging
BACKGROUND: A deep learning (DL) model that can automatically detect and classify cervical canal and neural foraminal stenosis using cervical spine magnetic resonance imaging (MRI) can improve diagnostic accuracy and efficiency.

Audio-visual aesthetic teaching methods in college students' vocal music teaching by deep learning.

Scientific reports
In recent times, characterized by the rapid advancement of science and technology, the educational system has continuously evolved. Within this modern educational landscape, Science, Technology, Engineering, Arts, and Mathematics (STEAM) education ha...

Extraction and evaluation of features of preterm patent ductus arteriosus in chest X-ray images using deep learning.

Scientific reports
Echocardiography is the gold standard of diagnosis and evaluation of patent ductus arteriosus (PDA), a common condition among preterm infants that can cause hemodynamic abnormalities and increased mortality rates, but this technique requires a skille...

Introducing µGUIDE for quantitative imaging via generalized uncertainty-driven inference using deep learning.

eLife
This work proposes µGUIDE: a general Bayesian framework to estimate posterior distributions of tissue microstructure parameters from any given biophysical model or signal representation, with exemplar demonstration in diffusion-weighted magnetic reso...

Effective Alzheimer's disease detection using enhanced Xception blending with snapshot ensemble.

Scientific reports
Alzheimer's disease (AD), a prevalent neurodegenerative disorder, leads to progressive dementia, which impairs decision-making, problem-solving, and communication. While there is no cure, early detection can facilitate treatments to slow its progress...

Deep learning-enhanced automated mitochondrial segmentation in FIB-SEM images using an entropy-weighted ensemble approach.

PloS one
Mitochondria are intracellular organelles that act as powerhouses by breaking down nutrition molecules to produce adenosine triphosphate (ATP) as cellular fuel. They have their own genetic material called mitochondrial DNA. Alterations in mitochondri...

A deep learning method for the recovery of standard-dose imaging quality from ultra-low-dose PET on wavelet domain.

European journal of nuclear medicine and molecular imaging
PURPOSE: Recent development in positron emission tomography (PET) dramatically increased the effective sensitivity by increasing the geometric coverage leading to total-body PET imaging. This encouraging breakthrough brings the hope of ultra-low dose...

Deep Learning Based Detection of Large Vessel Occlusions in Acute Ischemic Stroke Using High-Resolution Photon Counting Computed Tomography and Conventional Multidetector Computed Tomography.

Clinical neuroradiology
PURPOSE: Deep learning (DL) methods for detecting large vessel occlusion (LVO) in acute ischemic stroke (AIS) show promise, but the effect of computed tomography angiography (CTA) image quality on DL performance is unclear. Our study investigates the...