AI Medical Compendium Topic:
Pattern Recognition, Automated

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Reconstructing Perceived Images From Human Brain Activities With Bayesian Deep Multiview Learning.

IEEE transactions on neural networks and learning systems
Neural decoding, which aims to predict external visual stimuli information from evoked brain activities, plays an important role in understanding human visual system. Many existing methods are based on linear models, and most of them only focus on ei...

P_VggNet: A convolutional neural network (CNN) with pixel-based attention map.

PloS one
Attention maps have been fused in the VggNet structure (EAC-Net) [1] and have shown significant improvement compared to that of the VggNet structure. However, in [1], E-Net was designed based on the facial action unit (AU) center and for facial AU de...

Visualization Methods for Image Transformation Convolutional Neural Networks.

IEEE transactions on neural networks and learning systems
Convolutional neural networks (CNNs) are powerful machine learning models that have become the state of the art in several problems in the areas of computer vision and image processing. Nevertheless, the knowledge of why and how these models present ...

Automated Abdominal Segmentation of CT Scans for Body Composition Analysis Using Deep Learning.

Radiology
Purpose To develop and evaluate a fully automated algorithm for segmenting the abdomen from CT to quantify body composition. Materials and Methods For this retrospective study, a convolutional neural network based on the U-Net architecture was traine...

Morphometric analysis of peripheral myelinated nerve fibers through deep learning.

Journal of the peripheral nervous system : JPNS
Irrespective of initial causes of neurological diseases, these disorders usually exhibit two key pathological changes-axonal loss or demyelination or a mixture of the two. Therefore, vigorous quantification of myelin and axons is essential in studyin...

Face-from-Depth for Head Pose Estimation on Depth Images.

IEEE transactions on pattern analysis and machine intelligence
Depth cameras allow to set up reliable solutions for people monitoring and behavior understanding, especially when unstable or poor illumination conditions make unusable common RGB sensors. Therefore, we propose a complete framework for the estimatio...

Unsupervised Two-Path Neural Network for Cell Event Detection and Classification Using Spatiotemporal Patterns.

IEEE transactions on medical imaging
Automatic event detection in cell videos is essential for monitoring cell populations in biomedicine. Deep learning methods have advantages over traditional approaches for cell event detection due to their ability to capture more discriminative featu...

Deep convolutional networks do not classify based on global object shape.

PLoS computational biology
Deep convolutional networks (DCNNs) are achieving previously unseen performance in object classification, raising questions about whether DCNNs operate similarly to human vision. In biological vision, shape is arguably the most important cue for reco...

A Preliminary Study of Clinical Concept Detection Using Syntactic Relations.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Concept detection is an integral step in natural language processing (NLP) applications in the clinical domain. Clinical concepts are detailed (e.g., "pain in left/right upper/lower arm/leg") and expressed in diverse phrase types (e.g., noun, verb, a...

Discrimination of "hot potato voice" caused by upper airway obstruction utilizing a support vector machine.

The Laryngoscope
OBJECTIVES/HYPOTHESIS: "Hot potato voice" (HPV) is a thick, muffled voice caused by pharyngeal or laryngeal diseases characterized by severe upper airway obstruction, including acute epiglottitis and peritonsillitis. To develop a method for determini...