AIMC Topic: Attention

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Crowding and attention in a framework of neural network model.

Journal of vision
In this article, I present a framework that would accommodate the classic ideas of visual information processing together with more recent computational approaches. I used the current knowledge about visual crowding, capacity limitations, attention, ...

A Randomized Controlled Trial of an Intelligent Robotic Response to Joint Attention Intervention System.

Journal of autism and developmental disorders
Although there has been growing interest in utilizing robots for intervention in autism spectrum disorder (ASD), there have been very few controlled trials to assess the actual impacts of such systems on social communication vulnerabilities. This stu...

Deep Learning with Skip Connection Attention for Choroid Layer Segmentation in OCT Images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Since the thickness and shape of the choroid layer are indicators for the diagnosis of several ophthalmic diseases, the choroid layer segmentation is an important task. There exist many challenges in segmentation of the choroid layer. In this paper, ...

An Attention-Guided Deep Neural Network for Annotating Abnormalities in Chest X-ray Images: Visualization of Network Decision Basis.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Despite the potential of deep convolutional neural networks for classification of thorax diseases from chest X-ray images, this task is still challenging as it is categorized as a weakly supervised learning problem, and deep neural networks in genera...

Attention Networks for Multi-Task Signal Analysis.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Recent advances in deep learning have enabled the development of automated frameworks for analysing medical images and signals. For analysis of physiological recordings, models based on temporal convolutional networks and recurrent neural networks ha...

Predicting Age with Deep Neural Networks from Polysomnograms.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The aim of this study was to design a new deep learning framework for end-to-end processing of polysomnograms. This framework can be trained to analyze whole-night polysomnograms without the limitations of and bias towards clinical scoring guidelines...

EEG-based Depression Detection Using Convolutional Neural Network with Demographic Attention Mechanism.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Electroencephalography (EEG)-based depression detection has become a hot topic in the development of biomedical engineering. However, the complexity and nonstationarity of EEG signals are two biggest obstacles to this application. In addition, the ge...

Examining joint attention with the use of humanoid robots-A new approach to study fundamental mechanisms of social cognition.

Psychonomic bulletin & review
This article reviews methods to investigate joint attention and highlights the benefits of new methodological approaches that make use of the most recent technological developments, such as humanoid robots for studying social cognition. After reviewi...

Role of Instruction Adherence During Highly Structured Robotic Arm Training on Motor Outcomes for Individuals After Chronic Stroke.

American journal of physical medicine & rehabilitation
The aim of this study was to examine the effects of instruction adherence on upper limb motor outcomes after highly structured intervention. A secondary data analysis was completed using mixed linear modeling design. Thirty chronic stroke survivors w...

Identifying enhancer-promoter interactions with neural network based on pre-trained DNA vectors and attention mechanism.

Bioinformatics (Oxford, England)
MOTIVATION: Identification of enhancer-promoter interactions (EPIs) is of great significance to human development. However, experimental methods to identify EPIs cost too much in terms of time, manpower and money. Therefore, more and more research ef...