ArticleJournal of imaging2021
Variational Autoencoder for Image-Based Augmentation of Eye-Tracking Data.
Article in Journal of imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
15 citing papers in PubMed.
- Augmenting small tabular health data for training prognostic ensemble machine learning models using generative models.BMC medical informatics and decision making · 2025Article
- Transferable Contextual Network for Rural Road Extraction from UAV-Based Remote Sensing Images.Sensors (Basel, Switzerland) · 2025Article
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- CT image denoising methods for image quality improvement and radiation dose reduction.Journal of applied clinical medical physics · 2024Review
- Utilizing deep learning models in an intelligent eye-tracking system for autism spectrum disorder diagnosis.Frontiers in medicine · 2024Article
- How Artificial Intelligence Is Shaping Medical Imaging Technology: A Survey of Innovations and Applications.Bioengineering (Basel, Switzerland) · 2023Review
- Collagen fiber centerline tracking in fibrotic tissue via deep neural networks with variational autoencoder-based synthetic training data generation.Medical image analysis · 2023Article
- Enhancing the Breast Histopathology Image Analysis for Cancer Detection Using Variational Autoencoder.International journal of environmental research and public health · 2023Article
- Detection of abnormal extraocular muscles in small datasets of computed tomography images using a three-dimensional variational autoencoder.Scientific reports · 2023Article
- EVAE-Net: An Ensemble Variational Autoencoder Deep Learning Network for COVID-19 Classification Based on Chest X-ray Images.Diagnostics (Basel, Switzerland) · 2022Article
- Identifying Visual Attention Features Accurately Discerning Between Autism and Typically Developing: a Deep Learning Framework.Interdisciplinary sciences, computational life sciences · 2022Article
- Contribution of Synthetic Data Generation towards an Improved Patient Stratification in Palliative Care.Journal of personalized medicine · 2022Review
- A systematic comparison of generative models for medical images.International journal of computer assisted radiology and surgery · 2022Article
- Data augmentation using Variational Autoencoders for improvement of respiratory disease classification.PloS one · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Over the past decade, deep learning has achieved unprecedented successes in a diversity of application domains, given large-scale datasets. However, particular domains, such as healthcare, inherently suffer from data paucity and imbalance. Moreover, datasets could be largely inaccessible due to privacy concerns, or lack of data-sharing incentives. Such challenges have attached significance to the application of generative modeling and data augmentation in that domain. In this context, this study explores a machine learning-based approach for generating synthetic eye-tracking data. We explore a novel application of variational autoencoders (VAEs) in this regard. More specifically, a VAE model is trained to generate an image-based representation of the eye-tracking output, so-called scanpaths. Overall, our results validate that the VAE model could generate a plausible output from a limited dataset. Finally, it is empirically demonstrated that such approach could be employed as a mechanism for data augmentation to improve the performance in classification tasks.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.