Deep learning improves retinal fundus image analysis for eye diseases.
problem Improving accuracy in diagnosing eye diseases using retinal images.
method Review of deep learning models and datasets for retinal image analysis.
result Deep learning enhances detection and classification of eye diseases.
Simplified EEG analysis improves Parkinson's disease detection.
problem Improving accuracy in EEG-based Parkinson's disease diagnosis.
method Binary electrode grouping, Tsallis Entropy, and dual ec/eo EEG states.
result Binary grouping retains enough information for HC vs PD discrimination.
Deep learning predicts AMD progression from longitudinal fundus images.
problem Predicting future stages of age-related macular degeneration (AMD).
method InceptionV3 feature vectors, interval scaling, Recurrent Neural Network.
result 0.878 sensitivity, 0.887 specificity, 0.950 AUC.
Deep learning classifies keratoconus patients with high accuracy.
problem Accurately identifying keratoconus patients for early intervention.
method Unsupervised and semi-supervised machine learning models using corneal topography and clinical data.
result Unsupervised method with 29 variables shows better classification accuracy.
Proposes a neural network for dynamic risk prediction of AMD using longitudinal fundus images.
problem Dynamic risk prediction for progressive eye disorders like AMD.
method tdCoxSNN, a time-dependent Cox survival neural network integrating CNN.
result Demonstrates commendable predictive performance in AMD and PBC datasets.
Imaging fluorescent disease biomarkers in tissues and skin is a non-invasive method to screen for health conditions. We report an automated process that combines intraoral fluorescent porphyrin biomarker imaging, clinical examinations and machine learning for correlation of systemic health conditions with periodontal d…
Optimal model diagnoses funduscopic images for ocular diseases.
problem Binary classification of funduscopic images for ocular diseases.
method Transfer learning using Xception base architecture, Adam optimizer, mean squared error loss function, and custom heuristic equation.
result 90% accuracy, 94% sensitivity, and 86% specificity achieved.
Deep convolutional architecture identifies eye movements for biometric faster and more accurately.
problem Biometric identification of eye movements for authentication.
method Developed a deep convolutional architecture to process raw eye-tracking signals.
result Achieved a lower error rate by one order of magnitude and faster identification time by two orders of magnitude.
OpenEDS dataset captures eye images for VR applications.
problem Lack of high-quality eye image datasets for VR applications.
method Captured eye images using VR head-mounted display with synchronized cameras.
result Baseline experiment achieved 98.3% mIoU for pupil, iris, sclera segmentation.
Generative models of eye gaze help identify viewers from images.
problem Identifying viewers from images based on their eye movements.
method Derived Fisher kernels from generative models of eye gaze to train a discriminative classifier.
result Performance of the classifier improves with better underlying generative models.
Automated method selects eye tracking variables for categorization tasks.
problem Limited duration of infant cooperation and biases in handpicked eye tracking variables.
method Automated selection of eye tracking variables using statistical techniques.
result Same eye tracking variables classify category learners from non-learners in adults and infants with high accuracy.
Study uses machine learning to identify posturographic features in Parkinson's patients who fall.
problem Identifying patients at risk of falling in Parkinson's syndrome.
method Employed ts-AUC, a non-parametric multivariate two-sample test, to analyze posturographic features.
result Significant differences in antero-posterior movements and posturographic area between fallers and non-fallers identified.
New privacy method for eye tracking data reduces correlations and maintains accuracy.
problem Privacy concerns in eye tracking data from VR/AR glasses.
method Transform-coding based differential privacy mechanism for eye movement data.
result Significant reductions in sample correlations and query sensitivities, providing high privacy without loss in accuracy.
Machine learning aids in diagnosing Parkinson's disease with higher accuracy.
problem Subjectivity in traditional PD diagnosis methods and missed early symptoms.
method Machine learning applied to various data modalities for PD and control group classification.
result Machine learning methods show high potential for improving PD diagnosis.
Deep learning system improves diabetic retinopathy and macular edema grading.
problem Manual screening of diabetic retinopathy and macular edema images is labor-intensive and error-prone.
method Used deep learning on fundus images, achieving comparable or better performance than previous studies.
result Deep learning system can accurately classify images according to clinical grading scales.
Paper uses CNNs for eye tracking data segmentation, generation, and reconstruction.
problem Semantic segmentation and reconstruction of raw eye tracking data.
method Fully convolutional neural networks (FCNNs) for data segmentation and generation, variational auto-encoder for data generation.
result FCNNs can process any input size directly without preprocessing, generating raw eye tracking data.
The paper supports task classification from eye movements, achieving 95.4% accuracy.
problem Decoding the observer's task from eye movements.
method Exploratory analysis, feature projection, feature elimination, SVM and Ada Boosting classifier training.
result Achieved 95.4% accuracy in task classification.
Privacy-preserving eye tracking framework using synthetic images.
problem Preserving sensitive personal information in eye tracking technology.
method Randomized encoding for privacy, Support Vector Regression model training on synthetic eye images.
result Framework achieves real-time gaze estimation with same accuracy as non-private version.
Optical Coherence Tomography allows ophthalmologist to obtain cross-section imaging of eye retina. Assisted with digital image analysis methods, effective disease detection could be performed. Various methods exist to extract feature from OCT images. The proposed study aims to compare the effectiveness of handcrafted a…
Eye tracking measures ADHD-related working memory deficits.
problem Diagnosing ADHD in adults requires reliable measures of working memory capacity.
method Eye tracking technology and machine learning applied to a working memory task.
result Machine learning generated features unique to ADHD.
We study the problem of inferring readers' identities and estimating their level of text comprehension from observations of their eye movements during reading. We develop a generative model of individual gaze patterns (scanpaths) that makes use of lexical features of the fixated words. Using this generative model, we d…
Co-eye combines multiple symbolic representations to improve time series classification accuracy.
problem Challenges in time series classification due to domain diversity.
method Inspired by compound eyes, Co-eye uses multiple symbolic representations and hyper-parameterised lenses to classify time series data.
result Co-eye outperforms state-of-the-art techniques in accuracy and robustness across various domains.
Method identifies dual cognitive system interactions using eye-tracking data.
problem Quantify interactions between System 1 and System 2 cognitive processes.
method Modified Stroop test, machine learning, eye-tracking data.
result Machine learning can distinguish between different tasks and characterize attentional load.
1-D CNNs classify pupil size variations in scotopic conditions.
problem Handling wide inter-subjects variability in pupil analysis.
method 1-D Convolutional Neural Networks applied directly to raw pupil size data.
result 1-D CNNs provide high accuracy in classifying short-range pupil size sequences.
Synthesizing images of the eye fundus is a challenging task that has been previously approached by formulating complex models of the anatomy of the eye. New images can then be generated by sampling a suitable parameter space. In this work, we propose a method that learns to synthesize eye fundus images directly from da…
New benchmark for EEG-eye movement reconstruction from functional data.
problem Reconstructing eye movements from EEG data.
method Functional neural networks and open challenges for evaluation.
result Baseline results for consumer-grade and research-grade hardware.
Variable selection is a challenging issue in statistical applications when the number of predictors p far exceeds the number of observations n. In this ultra-high dimensional setting, the sure independence screening (SIS) procedure was introduced to significantly reduce the dimensionality by preserving the true mod…
ICODEN models survival data with interval-censored times using neural networks and ODEs.
problem Predicting time-to-event outcomes with interval-censored data, especially when models require strong assumptions or cannot handle high-dimensional predictors.
method ICODEN uses ordinary differential equations and deep neural networks to model the hazard function and cumulative hazard without proportional hazards assumption.
result ICODEN achieves satisfactory predictive accuracy across various simulation and real-world applications, handling high-dimensional predictors robustly.
Glaucoma is the second leading cause of blindness all over the world, with approximately 60 million cases reported worldwide in 2010. If undiagnosed in time, glaucoma causes irreversible damage to the optic nerve leading to blindness. The optic nerve head examination, which involves measurement of cup-to-disc ratio, is…
The paper studies the geometry of eye movements and cycles.
problem Understanding the visual stability and eye movement patterns.
method Develops differential geometry of saccades and saccadic cycles, characterizing them as geodesic segments and polygons.
result Provides necessary and sufficient conditions for a system of lines to be axes of rotation for saccades in a saccadic cycle.
Eye Movement analysis with Hidden Markov Models (EMHMM) is a method for modeling eye fixation sequences using hidden Markov models (HMMs). In this report, we run a simulation study to investigate the estimation error for learning HMMs with variational Bayesian inference, with respect to the number of sequences and the …
Interpretability is a key factor in the design of automatic classifiers for medical diagnosis. Deep learning models have been proven to be a very effective classification algorithm when trained in a supervised way with enough data. The main concern is the difficulty of inferring rationale interpretations from them. Dif…
The title says it all.
This paper introduces a novel approach to in-painting where the identity of the object to remove or change is preserved and accounted for at inference time: Exemplar GANs (ExGANs). ExGANs are a type of conditional GAN that utilize exemplar information to produce high-quality, personalized in painting results. We propos…
Large-scale eye-tracking dataset for Atari games.
problem Improving AI decision-making through human eye-tracking data.
method Recorded 117 hours of gameplay with eye movements from 20 Atari games.
result Human gameplay decisions and scores comparable to human records.
RED CoMETS improves multivariate time series classification accuracy.
problem Complexity of multivariate time series classification.
method Ensemble classifier RED CoMETS for symbolically represented multivariate time series.
result RED CoMETS achieves highest reported accuracy on 'HandMovementDirection' dataset.
We propose to model the fixation locations of the human eye when observing a still image by a Markovian point process in R 2 . Our approach is data driven using k-means clustering of the fixation locations to identify distinct salient regions of the image, which in turn correspond to the states of our Markov chain. Bay…
Method maps gaze fixations to hazards for safer construction.
problem Unrecognized hazards pose safety risks to construction workers.
method Instance segmentation using transfer learning.
result Mapped gaze data improves understanding of hazard recognition.
Deep neural networks classify T2D from retinal images with high accuracy.
problem Detecting early-stage Type 2 Diabetes from retinal images.
method Employed deep neural networks and multi-target learning to differentiate T2D from healthy individuals.
result Classification performance improved to AUC = 0.758 [±0.003] using images from both eyes. Evidence suggests Rapid-Eye-Movement (REM) Sleep Behaviour Disorder (RBD) is an early predictor of Parkinson's disease. This study proposes a fully-automated framework for RBD detection consisting of automated sleep staging followed by RBD identification. Analysis was assessed using a limited polysomnography montage fr…
Paper finds gender classification accuracy varies by skin type, not ethnicity.
problem Unequal performance of face classification services across skin types and genders.
method Stability experiments, image manipulation, and post-hoc explanation techniques.
result Lip, eye, and cheek structure differences, not skin type, cause gender classification discrepancies.
This paper uses RNN to speed up high-speed channel simulation.
problem Computational intensity of generating eye diagrams in high-speed channels.
method Trains a recurrent neural network (RNN) to generate black-box macromodels.
result Reduces computation time significantly without complex simulations.
Paper uses deep Q-network to correct eye movement artifacts in OCT volumes.
problem Unsupervised correction of inter-frame misalignments in OCT volumes due to eye movement artifacts.
method Dueling deep Q-network trained to maximize reward signals based on intensity-based image similarity metrics.
result Average normalized mutual information and correlation coefficient of 0.985 and 0.914, respectively.
DPVis integrates HMMs into visualizations for disease progression analysis.
problem Challenges in interpreting HMMs for disease progression modeling.
method Design study with clinical experts, visualizations of HMM parameters and outcomes.
result DPVis successfully evaluates and summarizes disease progression models.
SS3M learns disease phenotypes from few labels.
problem Lack of supervised data for disease phenotyping.
method Semi-Supervised Mixed Membership Model (SS3M).
result SS3M learns interpretable disease phenotypes.
This is an introduction to the algebraic aspect of Teichmüller dynamics, with a focus on its interplay with the geometry of moduli spaces of curves as well as recent advances in the field.
Bayesian hypergraph inference models disease pathways from EHR data.
problem Modeling rare diseases influenced by shared risk factors.
method Bayesian hypergraph inference framework reframing multi-disease modeling.
result Interpretable disease pathways and well-calibrated uncertainty quantification.
Model learns disease self-representations for drug repositioning.
problem Drug repositioning for disease treatment.
method Enforces proximity in disease self-representations to preserve human phenome network structure.
result Method outperforms state-of-the-art approaches and produces biologically interpretable disease self-representations.