scholarly journals Strabismus Recognition Using Eye-Tracking Data and Convolutional Neural Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Zenghai Chen ◽  
Hong Fu ◽  
Wai-Lun Lo ◽  
Zheru Chi
Keyword(s):  
Neural Networks ◽  
Eye Tracking ◽  
Convolutional Neural Networks ◽  
Vision Loss ◽  
Normal Subjects ◽  
Labor Cost ◽  
Image Features ◽  
Natural Image ◽  
Tracking Data ◽  
Eye Tracker

Strabismus is one of the most common vision diseases that would cause amblyopia and even permanent vision loss. Timely diagnosis is crucial for well treating strabismus. In contrast to manual diagnosis, automatic recognition can significantly reduce labor cost and increase diagnosis efficiency. In this paper, we propose to recognize strabismus using eye-tracking data and convolutional neural networks. In particular, an eye tracker is first exploited to record a subject’s eye movements. A gaze deviation (GaDe) image is then proposed to characterize the subject’s eye-tracking data according to the accuracies of gaze points. The GaDe image is fed to a convolutional neural network (CNN) that has been trained on a large image database called ImageNet. The outputs of the full connection layers of the CNN are used as the GaDe image’s features for strabismus recognition. A dataset containing eye-tracking data of both strabismic subjects and normal subjects is established for experiments. Experimental results demonstrate that the natural image features can be well transferred to represent eye-tracking data, and strabismus can be effectively recognized by our proposed method.

scholarly journals Detection of liver cirrhosis in standard T2-weighted MRI using deep transfer learning

2021 ◽  
Author(s):  
Sebastian Nowak ◽  
Narine Mesropyan ◽  
Anton Faron ◽  
Wolfgang Block ◽  
Martin Reuter ◽  
...  
Keyword(s):  
Neural Networks ◽  
Liver Cirrhosis ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Classification Accuracy ◽  
Automatic Segmentation ◽  
Analysis Data ◽  
Caudate Lobe ◽  
Natural Image ◽  
Liver Segmentation

Abstract Objectives To investigate the diagnostic performance of deep transfer learning (DTL) to detect liver cirrhosis from clinical MRI. Methods The dataset for this retrospective analysis consisted of 713 (343 female) patients who underwent liver MRI between 2017 and 2019. In total, 553 of these subjects had a confirmed diagnosis of liver cirrhosis, while the remainder had no history of liver disease. T2-weighted MRI slices at the level of the caudate lobe were manually exported for DTL analysis. Data were randomly split into training, validation, and test sets (70%/15%/15%). A ResNet50 convolutional neural network (CNN) pre-trained on the ImageNet archive was used for cirrhosis detection with and without upstream liver segmentation. Classification performance for detection of liver cirrhosis was compared to two radiologists with different levels of experience (4th-year resident, board-certified radiologist). Segmentation was performed using a U-Net architecture built on a pre-trained ResNet34 encoder. Differences in classification accuracy were assessed by the χ2-test. Results Dice coefficients for automatic segmentation were above 0.98 for both validation and test data. The classification accuracy of liver cirrhosis on validation (vACC) and test (tACC) data for the DTL pipeline with upstream liver segmentation (vACC = 0.99, tACC = 0.96) was significantly higher compared to the resident (vACC = 0.88, p < 0.01; tACC = 0.91, p = 0.01) and to the board-certified radiologist (vACC = 0.96, p < 0.01; tACC = 0.90, p < 0.01). Conclusion This proof-of-principle study demonstrates the potential of DTL for detecting cirrhosis based on standard T2-weighted MRI. The presented method for image-based diagnosis of liver cirrhosis demonstrated expert-level classification accuracy. Key Points • A pipeline consisting of two convolutional neural networks (CNNs) pre-trained on an extensive natural image database (ImageNet archive) enables detection of liver cirrhosis on standard T2-weighted MRI. • High classification accuracy can be achieved even without altering the pre-trained parameters of the convolutional neural networks. • Other abdominal structures apart from the liver were relevant for detection when the network was trained on unsegmented images.

scholarly journals Depth in convolutional neural networks solves scene segmentation

2019 ◽  
Author(s):  
N Seijdel ◽  
N Tsakmakidis ◽  
EHF De Haan ◽  
SM Bohte ◽  
HS Scholte
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Visual Processing ◽  
Human Performance ◽  
Object Identification ◽  
Image Features ◽  
Background Information ◽  
Natural Scenes ◽  
Scene Segmentation ◽  
Deep Convolutional Neural Networks

AbstractFeedforward deep convolutional neural networks (DCNNs) are, under specific conditions, matching and even surpassing human performance in object recognition in natural scenes. This performance suggests that the analysis of a loose collection of image features could support the recognition of natural object categories, without dedicated systems to solve specific visual subtasks. Research in humans however suggests that while feedforward activity may suffice for sparse scenes with isolated objects, additional visual operations (‘routines’) that aid the recognition process (e.g. segmentation or grouping) are needed for more complex scenes. Linking human visual processing to performance of DCNNs with increasing depth, we here explored if, how, and when object information is differentiated from the backgrounds they appear on. To this end, we controlled the information in both objects and backgrounds, as well as the relationship between them by adding noise, manipulating background congruence and systematically occluding parts of the image. Results indicate that with an increase in network depth, there is an increase in the distinction between object- and background information. For more shallow networks, results indicated a benefit of training on segmented objects. Overall, these results indicate that, de facto, scene segmentation can be performed by a network of sufficient depth. We conclude that the human brain could perform scene segmentation in the context of object identification without an explicit mechanism, by selecting or “binding” features that belong to the object and ignoring other features, in a manner similar to a very deep convolutional neural network.

scholarly journals Predicting the future direction of cell movement with convolutional neural networks

2018 ◽  
Author(s):  
Shori Nishimoto ◽  
Yuta Tokuoka ◽  
Takahiro G Yamada ◽  
Noriko F Hiroi ◽  
Akira Funahashi
Keyword(s):  
Neural Networks ◽  
Cell Fate ◽  
Convolutional Neural Networks ◽  
Cell Shape ◽  
Cell Movement ◽  
Image Features ◽  
Morphological Features ◽  
Current Cell ◽  
The Future ◽  
Future Direction

SummaryImage-based deep learning systems, such as convolutional neural networks (CNNs), have recently been applied to cell classification, producing impressive results; however, application of CNNs has been confined to classification of the current cell state from the image. Here, we focused on cell movement where current and/or past cell shape can influence the future cell fate. We demonstrate that CNNs prospectively predicted the future direction of cell movement with high accuracy from a single image patch of a cell at a certain time. Furthermore, by visualizing the image features that were learned by the CNNs, we could identify morphological features, e.g., the protrusions and trailing edge that have been experimentally reported to determine the direction of cell movement. Our results indicate that CNNs have the potential to predict the future cell fate from current cell shape, and can be used to automatically identify those morphological features that influence future cell fate.

Deep Transfer Learning for Histopathological Diagnosis of Cervical Cancer Using Convolutional Neural Networks with Visualization Schemes

2020 ◽  
Vol 10 (2) ◽  
pp. 391-400 ◽  
Author(s):  
Ying Chen ◽  
Xiaomin Qin ◽  
Jingyu Xiong ◽  
Shugong Xu ◽  
Jun Shi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Cervical Cancer ◽  
Learning Strategies ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Characteristic Curve ◽  
Disease Classification ◽  
Natural Image ◽  
Histopathological Diagnosis ◽  
The Impact

This study aimed to propose a deep transfer learning framework for histopathological image analysis by using convolutional neural networks (CNNs) with visualization schemes, and to evaluate its usage for automated and interpretable diagnosis of cervical cancer. First, in order to examine the potential of the transfer learning for classifying cervix histopathological images, we pre-trained three state-of-the-art CNN architectures on large-size natural image datasets and then fine-tuned them on small-size histopathological datasets. Second, we investigated the impact of three learning strategies on classification accuracy. Third, we visualized both the multiple-layer convolutional kernels of CNNs and the regions of interest so as to increase the clinical interpretability of the networks. Our method was evaluated on a database of 4993 cervical histological images (2503 benign and 2490 malignant). The experimental results demonstrated that our method achieved 95.88% sensitivity, 98.93% specificity, 97.42% accuracy, 94.81% Youden's index and 99.71% area under the receiver operating characteristic curve. Our method can reduce the cognitive burden on pathologists for cervical disease classification and improve their diagnostic efficiency and accuracy. It may be potentially used in clinical routine for histopathological diagnosis of cervical cancer.

scholarly journals The Power of Eye Tracking in Economics Experiments

2016 ◽  
Vol 106 (5) ◽  
pp. 309-313 ◽  
Author(s):  
Joanna N. Lahey ◽  
Douglas Oxley
Keyword(s):  
Economic Theory ◽  
Data Analysis ◽  
Eye Tracking ◽  
Data Management ◽  
Tracking Data ◽  
Eye Tracker ◽  
Hiring Decisions ◽  
Economics Experiments ◽  
Set Up ◽  
Tracking Technology

Eye tracking is a technology that tracks eye activity including how long and where a participant is looking. As eye tracking technology has improved and become more affordable its use has expanded. We discuss how to design, implement, and analyze an experiment using this technology to study economic theory. Using our experience fielding an experiment to study hiring decisions we guide the reader through how to choose an eye tracker, concerns with participants and set-up, types of outputs, limitations of eye tracking, data management and data analysis. We conclude with suggestions for combining eye tracking with other measurements.

scholarly journals Algorithms for the automated correction of vertical drift in eye-tracking data

2021 ◽  
Author(s):  
Jon W. Carr ◽  
Valentina N. Pescuma ◽  
Michele Furlan ◽  
Maria Ktori ◽  
Davide Crepaldi
Keyword(s):  
Eye Tracking ◽  
Vertical Axis ◽  
Great Promise ◽  
Tracking Data ◽  
Eye Tracker ◽  
Algorithm Selection ◽  
Vertical Drift ◽  
Gradual Loss ◽  
Adjacent Line ◽  
Dynamic Time

AbstractA common problem in eye-tracking research is vertical drift—the progressive displacement of fixation registrations on the vertical axis that results from a gradual loss of eye-tracker calibration over time. This is particularly problematic in experiments that involve the reading of multiline passages, where it is critical that fixations on one line are not erroneously recorded on an adjacent line. Correction is often performed manually by the researcher, but this process is tedious, time-consuming, and prone to error and inconsistency. Various methods have previously been proposed for the automated, post hoc correction of vertical drift in reading data, but these methods vary greatly, not just in terms of the algorithmic principles on which they are based, but also in terms of their availability, documentation, implementation languages, and so forth. Furthermore, these methods have largely been developed in isolation with little attempt to systematically evaluate them, meaning that drift correction techniques are moving forward blindly. We document ten major algorithms, including two that are novel to this paper, and evaluate them using both simulated and natural eye-tracking data. Our results suggest that a method based on dynamic time warping offers great promise, but we also find that some algorithms are better suited than others to particular types of drift phenomena and reading behavior, allowing us to offer evidence-based advice on algorithm selection.

scholarly journals Investigating the Use of Eye Fixation Data for Emotion Classification in VR

2021 ◽  
Vol 12 (3) ◽  
pp. 1852-1857
Author(s):  
Lim Jia Zheng Et.al
Keyword(s):  
Eye Tracking ◽  
Classification Accuracy ◽  
Preliminary Investigation ◽  
Support Vector ◽  
Tracking Data ◽  
Eye Tracker ◽  
Emotion Classification ◽  
Learning Classifier ◽  
Rbf Kernel ◽  
Tracking Technology

Eye-tracking technology has become popular recently and widely used in research on emotion recognition since its usability. In this paper, we presented a preliminary investigation on a novelty approach for detecting emotions using eye-tracking data in virtual reality (VR) to classify 4-quadrant of emotions according to russell’scircumplex model of affects. A presentation of 3600 videos is used as the experiment stimuli to evoke the emotions of the user in VR. An add-on eye-tracker within the VR headset is used for the recording and collecting device of eye-tracking data. Fixation data is extracted and chosen as the eye feature used in this investigation. The machine learning classifier is support vector machine (SVM) with radial basis function (RBF) kernel. The best classification accuracy achieved is 69.23%. The findings showed that emotion classification using fixation data has promising results in the prediction accuracy from a four-class random classification.

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