Human Blastocyst Image Generation Using Generative Adversarial Networks

Several medical fields such as In Vitro Fertilization (IVF) suffer from limited databases d require augmentations, to be suitable for machine learning (ML) tasks such as classification. We test the use of various Generative Adversarial Network (GAN) architectures to tackle the problem of limited dat...

Full description

Saved in:
Bibliographic Details
Main Authors: Tikas, Evangelos, Iliadis, Lazaros Alexios, Sotiroudis, Sotirios, Boursianis, Achilles, Kokkinidis, Konstantinos-Iraklis D., Papatheodorou, Achilleas, Goudos, Sotirios K.
Format: Conference Proceeding
Language:English
Subjects:
Deep Learning
Generative adversarial networks
Generative AI
In vitro fertilization
Machine learning
Measurement
Probability density function
Synthetic Images
Training
Visualization
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Several medical fields such as In Vitro Fertilization (IVF) suffer from limited databases d require augmentations, to be suitable for machine learning (ML) tasks such as classification. We test the use of various Generative Adversarial Network (GAN) architectures to tackle the problem of limited data by generating high-quality samples. We compare two GAN-based data augmentation techniques for a limited medical image database, utilizing synthetic images. A GAN can create synthetic images by learning to model a probability density function that can represent large data like images. Synthetic samples are then evaluated using metrics and visual evaluation with respect to the original "real" samples.
ISSN:1945-8452
DOI:10.1109/ISBI56570.2024.10635904