AI Medical Compendium Topic

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Eye Movements

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Eye Tracking for Deep Learning Segmentation Using Convolutional Neural Networks.

Journal of digital imaging
Deep learning with convolutional neural networks (CNNs) has experienced tremendous growth in multiple healthcare applications and has been shown to have high accuracy in semantic segmentation of medical (e.g., radiology and pathology) images. However...

Multimodal Emotion Recognition from Eye Image, Eye Movement and EEG Using Deep Neural Networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
In consideration of the complexity of recording electroencephalography(EEG), some researchers are trying to find new features of emotion recognition. In order to investigate the potential of eye tracking glasses for multimodal emotion recognition, we...

In natural interaction with embodied robots, we prefer it when they follow our gaze: a gaze-contingent mobile eyetracking study.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Initiating joint attention by leading someone's gaze is a rewarding experience which facilitates social interaction. Here, we investigate this experience of leading an agent's gaze while applying a more realistic paradigm than traditional screen-base...

gazeNet: End-to-end eye-movement event detection with deep neural networks.

Behavior research methods
Existing event detection algorithms for eye-movement data almost exclusively rely on thresholding one or more hand-crafted signal features, each computed from the stream of raw gaze data. Moreover, this thresholding is largely left for the end user. ...

Learning From Peers' Eye Movements in the Absence of Expert Guidance: A Proof of Concept Using Laboratory Stock Trading, Eye Tracking, and Machine Learning.

Cognitive science
Existing research shows that people can improve their decision skills by learning what experts paid attention to when faced with the same problem. However, in domains like financial education, effective instruction requires frequent, personalized fee...

Scanpath modeling and classification with hidden Markov models.

Behavior research methods
How people look at visual information reveals fundamental information about them; their interests and their states of mind. Previous studies showed that scanpath, i.e., the sequence of eye movements made by an observer exploring a visual stimulus, ca...

Using machine learning to detect events in eye-tracking data.

Behavior research methods
Event detection is a challenging stage in eye movement data analysis. A major drawback of current event detection methods is that parameters have to be adjusted based on eye movement data quality. Here we show that a fully automated classification of...

A parametric texture model based on deep convolutional features closely matches texture appearance for humans.

Journal of vision
Our visual environment is full of texture-"stuff" like cloth, bark, or gravel as distinct from "things" like dresses, trees, or paths-and humans are adept at perceiving subtle variations in material properties. To investigate image features important...

Towards free 3D end-point control for robotic-assisted human reaching using binocular eye tracking.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]
Eye-movements are the only directly observable behavioural signals that are highly correlated with actions at the task level, and proactive of body movements and thus reflect action intentions. Moreover, eye movements are preserved in many movement d...

Using support vector machines to identify literacy skills: Evidence from eye movements.

Behavior research methods
Is inferring readers' literacy skills possible by analyzing their eye movements during text reading? This study used Support Vector Machines (SVM) to analyze eye movement data from 61 undergraduate students who read a multiple-paragraph, multiple-top...