Data Augmentation with Cross-Modal Variational Autoencoders (<tt>DACMVA</tt>) for Cancer Survival Prediction

Data Augmentation with Cross-Modal Variational Autoencoders (<tt>DACMVA</tt>) 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
Main Authors: Sara Rajaram, Cassie S. Mitchell
Format: Article
Language:English
Published: MDPI AG 2023-12-01
Series:Information
Subjects:
data augmentation
Variational Autoencoder
Generative Adversarial Network
cancer survival prediction
Online Access:https://www.mdpi.com/2078-2489/15/1/7

Internet

https://www.mdpi.com/2078-2489/15/1/7

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