Data Augmentation with Cross-Modal Variational Autoencoders (DACMVA) for Cancer Survival Prediction

The ability to translate Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) into different modalities and data types is essential to improve Deep Learning (DL) for predictive medicine. This work presents DACMVA, a novel framework to conduct data augmentation in a cross-modal...

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Bibliographic Details
Published in:	Information (Basel) Vol. 15; no. 1; p. 7
Main Authors:	Rajaram, Sara, Mitchell, Cassie S.
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 01.01.2024
Subjects:	Cancer cancer survival prediction Data augmentation Datasets Gene expression Generative Adversarial Network Generative adversarial networks Machine learning Medical research Missing data Oversampling Performance enhancement Rank tests Survival Tables (data) Tomography Variational Autoencoder cancer survival prediction Generative Adversarial Network data augmentation Variational Autoencoder
ISSN:	2078-2489, 2078-2489
Online Access:	Get full text
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