AI Medical Compendium Topic:
Pattern Recognition, Automated

Clear Filters Showing 631 to 640 of 1638 articles

A proposal of prior probability-oriented clustering in feature encoding strategies.

PloS one
Codebook-based feature encodings are a standard framework for image recognition issues. A codebook is usually constructed by clusterings, such as the k-means and the Gaussian Mixture Model (GMM). A codebook size is an important factor to decide the t...

Deep learning for image analysis: Personalizing medicine closer to the point of care.

Critical reviews in clinical laboratory sciences
The precision-based revolution in medicine continues to demand stratification of patients into smaller and more personalized subgroups. While genomic technologies have largely led this movement, diagnostic results can take days to weeks to generate. ...

Robust Single-Sample Face Recognition by Sparsity-Driven Sub-Dictionary Learning Using Deep Features.

Sensors (Basel, Switzerland)
Face recognition using a single reference image per subject is challenging, above all when referring to a large gallery of subjects. Furthermore, the problem hardness seriously increases when the images are acquired in unconstrained conditions. In th...

A Two-Timescale Duplex Neurodynamic Approach to Biconvex Optimization.

IEEE transactions on neural networks and learning systems
This paper presents a two-timescale duplex neurodynamic system for constrained biconvex optimization. The two-timescale duplex neurodynamic system consists of two recurrent neural networks (RNNs) operating collaboratively at two timescales. By operat...

An Attention-Based Spiking Neural Network for Unsupervised Spike-Sorting.

International journal of neural systems
Bio-inspired computing using artificial spiking neural networks promises performances outperforming currently available computational approaches. Yet, the number of applications of such networks remains limited due to the absence of generic training ...

Learning a discriminant graph-based embedding with feature selection for image categorization.

Neural networks : the official journal of the International Neural Network Society
Graph-based embedding methods are very useful for reducing the dimension of high-dimensional data and for extracting their relevant features. In this paper, we introduce a novel nonlinear method called Flexible Discriminant graph-based Embedding with...

Integrative Gene Selection on Gene Expression Data: Providing Biological Context to Traditional Approaches.

Journal of integrative bioinformatics
The advance of high-throughput RNA-Sequencing techniques enables researchers to analyze the complete gene activity in particular cells. From the insights of such analyses, researchers can identify disease-specific expression profiles, thus understand...

Fast animal pose estimation using deep neural networks.

Nature methods
The need for automated and efficient systems for tracking full animal pose has increased with the complexity of behavioral data and analyses. Here we introduce LEAP (LEAP estimates animal pose), a deep-learning-based method for predicting the positio...

PENYEK: Automated brown planthopper detection from imperfect sticky pad images using deep convolutional neural network.

PloS one
Rice is a staple food in Asia and it contributes significantly to the Gross Domestic Product (GDP) of Malaysia and other developing countries. Brown Planthopper (BPH) causes high levels of economic loss in Malaysia. Identification of BPH presence and...

The Whole Is More Than Its Parts? From Explicit to Implicit Pose Normalization.

IEEE transactions on pattern analysis and machine intelligence
Fine-grained classification describes the automated recognition of visually similar object categories like birds species. Previous works were usually based on explicit pose normalization, i.e., the detection and description of object parts. However, ...