scholarly journals State of the Art: Eye-Tracking Studies in Medical Imaging

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 37023-37034 ◽  
Author(s):  
Lucie Leveque ◽  
Hilde Bosmans ◽  
Lesley Cockmartin ◽  
Hantao Liu
Keyword(s):  
Medical Imaging ◽  
Eye Tracking ◽  
State Of The Art

scholarly journals Accelerating eye movement research via accurate and affordable smartphone eye tracking

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Nachiappan Valliappan ◽  
Na Dai ◽  
Ethan Steinberg ◽  
Junfeng He ◽  
Kantwon Rogers ◽  
...  
Keyword(s):  
Machine Learning ◽  
Eye Tracking ◽  
Eye Movement ◽  
Vision Research ◽  
State Of The Art ◽  
Movement Behavior ◽  
Natural Image ◽  
Explicit Consent ◽  
Using Data ◽  
Additional Hardware

Abstract Eye tracking has been widely used for decades in vision research, language and usability. However, most prior research has focused on large desktop displays using specialized eye trackers that are expensive and cannot scale. Little is known about eye movement behavior on phones, despite their pervasiveness and large amount of time spent. We leverage machine learning to demonstrate accurate smartphone-based eye tracking without any additional hardware. We show that the accuracy of our method is comparable to state-of-the-art mobile eye trackers that are 100x more expensive. Using data from over 100 opted-in users, we replicate key findings from previous eye movement research on oculomotor tasks and saliency analyses during natural image viewing. In addition, we demonstrate the utility of smartphone-based gaze for detecting reading comprehension difficulty. Our results show the potential for scaling eye movement research by orders-of-magnitude to thousands of participants (with explicit consent), enabling advances in vision research, accessibility and healthcare.

scholarly journals Non-I.I.D. Multi-Instance Learning for Predicting Instance and Bag Labels with Variational Auto-Encoder

2021 ◽  
Author(s):  
Weijia Zhang
Keyword(s):  
Medical Imaging ◽  
Supervised Learning ◽  
Real World ◽  
State Of The Art ◽  
Experimental Results ◽  
Weakly Supervised Learning ◽  
Variational Autoencoder ◽  
Label Prediction ◽  
Weakly Supervised ◽  
Better Than

Multi-instance learning is a type of weakly supervised learning. It deals with tasks where the data is a set of bags and each bag is a set of instances. Only the bag labels are observed whereas the labels for the instances are unknown. An important advantage of multi-instance learning is that by representing objects as a bag of instances, it is able to preserve the inherent dependencies among parts of the objects. Unfortunately, most existing algorithms assume all instances to be identically and independently distributed, which violates real-world scenarios since the instances within a bag are rarely independent. In this work, we propose the Multi-Instance Variational Autoencoder (MIVAE) algorithm which explicitly models the dependencies among the instances for predicting both bag labels and instance labels. Experimental results on several multi-instance benchmarks and end-to-end medical imaging datasets demonstrate that MIVAE performs better than state-of-the-art algorithms for both instance label and bag label prediction tasks.

Pupil detection for head-mounted eye tracking in the wild: an evaluation of the state of the art

2016 ◽  
Vol 27 (8) ◽  
pp. 1275-1288 ◽  
Author(s):  
Wolfgang Fuhl ◽  
Marc Tonsen ◽  
Andreas Bulling ◽  
Enkelejda Kasneci
Keyword(s):  
Eye Tracking ◽  
State Of The Art ◽  
The State ◽  
Pupil Detection ◽  
In The Wild

scholarly journals The Core of Medical Imaging: State of the Art and Perspectives on the Detectors

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1642
Author(s):  
Maria Filomena Santarelli ◽  
Giulio Giovannetti ◽  
Valentina Hartwig ◽  
Simona Celi ◽  
Vincenzo Positano ◽  
...  
Keyword(s):  
Medical Imaging ◽  
Thermal Imaging ◽  
Near Infrared ◽  
New Technologies ◽  
State Of The Art ◽  
Single Photon ◽  
Imaging Techniques ◽  
Photon Emission ◽  
Emission Tomography ◽  
Positron Emission

In this review, the roles of detectors in various medical imaging techniques were described. Ultrasound, optical (near-infrared spectroscopy and optical coherence tomography) and thermal imaging, magnetic resonance imaging, computed tomography, single-photon emission tomography, positron emission tomography were the imaging modalities considered. For each methodology, the state of the art of detectors mainly used in the systems was described, emphasizing new technologies applied.

scholarly journals Medical Imaging of Neurologic Disease: State of the Art

1991 ◽  
Vol 4 (1) ◽  
pp. 5-14
Author(s):  
Paul T. Siemers ◽  
Peter G. Hildenbrand ◽  
George E. Plum ◽  
Steven E. Harms ◽  
Sheff D. Olinger ◽  
...  
Keyword(s):  
Medical Imaging ◽  
State Of The Art ◽  
Disease State ◽  
Neurologic Disease
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