Generative Models Based on VAE and GAN for New Medical Data Synthesis: Chap.; Society 5.0: Cyberspace for Advanced Human-Centered Society; Vol. 333 : Studies in Systems, Decision and Control (SSDC)

Generative Models Based on VAE and GAN for New Medical Data Synthesis: Chap.; Society 5.0: Cyberspace for Advanced Human-Centered Society; Vol. 333 : Studies in Systems, Decision and Control (SSDC)

Bibliographic Details
Parent link:Society 5.0: Cyberspace for Advanced Human-Centered Society/ eds. A. G. Kravets, A. A. Bolshakov, M. Shcherbakov
Vol. 333 : Studies in Systems, Decision and Control (SSDC).— 2021.— [P. 217-226]
Main Author: Laptev V. V. Vladislav Vitaljevich
Corporate Authors: Национальный исследовательский Томский политехнический университет Инженерная школа информационных технологий и робототехники Отделение информационных технологий, Национальный исследовательский Томский политехнический университет Школа базовой инженерной подготовки Отделение иностранных языков
Other Authors: Gerget O. M. Olga Mikhailovna, Markova N. A. Natalia Aleksandrovna
Summary:Title screen
The chapter deals with the construction of generative models using Variational Autoencoder (VAE) and Generative Adversarial Neural Networks to synthesize new medical data. VAE is a synthesis of two complete neural networks: an encoder E and a generator G, as well as the latent space connecting them and enabling them to carry out random transformation and interpolation. Generative Adversarial Nets (GAN) in their turn are built on the principle of interaction between a generative model (generator G) and a discriminating model (discriminator D). When creating generator G (both VAE and GAN), its architecture of a neural network based on convolutional layers, with the application of the new deep learning framework Tensorflow-addons is used. As E and D encoders, respectively, the models of transfer learning, problem domain-image feature vector are used in the work. The comparison between them is made in the chapter and the most optimal model for solving the proposed problem is selected. The chapter presents the results of the research obtained on the basis of VAE and GAN implementation.
Режим доступа: по договору с организацией-держателем ресурса
Language:English
Published: 2021
Subjects:
электронный ресурс
труды учёных ТПУ
generative adversarial nets
variational autoencoder
convolution neural network
generation
transfer learning
discrimination
distribution
probability
training
автоэнкодеры
сверточные нейронные сети
дискриминация
вероятности
Online Access:https://doi.org/10.1007/978-3-030-63563-3_17
Format: Electronic Book Chapter
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=664664

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