AI Medical Compendium Topic

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Attention

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Saliency From Growing Neural Gas: Learning Pre-Attentional Structures for a Flexible Attention System.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Artificial visual attention has been an active research area for over two decades. Especially, the concept of saliency has been implemented in many different ways. Early approaches aimed at closely modeling saliency processing with concepts from biol...

Evaluate driver response to active warning system in level-2 automated vehicles.

Accident; analysis and prevention
As vehicles with automated functions become more prevalent on U.S. roadways, maintaining driver attention while the vehicle is engaged in automation will be an important consideration for safe operation of these vehicles. The objective of this paper ...

Influence of New Technologies on Post-Stroke Rehabilitation: A Comparison of Armeo Spring to the Kinect System.

Medicina (Kaunas, Lithuania)
BACKGROUND: New technologies to improve post-stroke rehabilitation outcomes are of great interest and have a positive impact on functional, motor, and cognitive recovery. Identifying the most effective rehabilitation intervention is a recognized prio...

A deep learning model incorporating part of speech and self-matching attention for named entity recognition of Chinese electronic medical records.

BMC medical informatics and decision making
BACKGROUND: The Named Entity Recognition (NER) task as a key step in the extraction of health information, has encountered many challenges in Chinese Electronic Medical Records (EMRs). Firstly, the casual use of Chinese abbreviations and doctors' per...

Attention-based deep residual learning network for entity relation extraction in Chinese EMRs.

BMC medical informatics and decision making
BACKGROUND: Electronic medical records (EMRs) contain a variety of valuable medical concepts and relations. The ability to recognize relations between medical concepts described in EMRs enables the automatic processing of clinical texts, resulting in...

Emotional arousal amplifies competitions across goal-relevant representation: A neurocomputational framework.

Cognition
Emotional arousal often facilitates memory for some aspects of an event while impairing memory for other aspects of the same event. Across three experiments, we found that emotional arousal amplifies competition among goal-relevant representations, s...

IARNN-Based Semantic-Containing Double-Level Embedding Bi-LSTM for Question-and-Answer Matching.

Computational intelligence and neuroscience
We propose a novel end-to-end approach, namely, the semantic-containing double-level embedding Bi-LSTM model (SCDE-Bi-LSTM), to solve the three key problems of Q&A matching in the Chinese medical field. In the similarity calculation of the Q&A core m...

Deep Attention Network for Egocentric Action Recognition.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Recognizing a camera wearer's actions from videos captured by an egocentric camera is a challenging task. In this paper, we employ a two-stream deep neural network composed of an appearance-based stream and a motion-based stream to recognize egocentr...

A unified computational framework for visual attention dynamics.

Progress in brain research
Eye movements are an essential part of human vision as they drive the fovea and, consequently, selective visual attention toward a region of interest in space. Free visual exploration is an inherently stochastic process depending on image statistics ...

Predicting Human Saccadic Scanpaths Based on Iterative Representation Learning.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Visual attention is a dynamic process of scene exploration and information acquisition. However, existing research on attention modeling has concentrated on estimating static salient locations. In contrast, dynamic attributes presented by saccade hav...