Please use this identifier to cite or link to this item:
Title: Generating synthetic intestine images
Author: Ivanov, Stefan
Director/Tutor: Seguí Mesquida, Santi
Keywords: Gastroscòpia
Aprenentatge automàtic
Treballs de fi de màster
Algorismes computacionals
Xarxes neuronals (Informàtica)
Machine learning
Master's theses
Algoritmos computacionales
Neural networks (Computer science)
Issue Date: 28-Jun-2019
Abstract: [en] Capsule endoscopy is a non-invasive medical procedure used to record images of the gastrointestinal tract. While this method is a better alternative for patients, it presents a difficulty to doctors who need to go over as much as 50000 images. Scientists are developing machine learning algorithms that will automatically throw away images free of any anomalies. Like other medical applications, however, available data to train such models is sparse. Therefore, we attempt to create synthetic images that can be used as substitution. For the purpose we have used generative adversarial networks (GANs) as they have recently shown great promise for problems like this one. Training a classifier on both the real and synthetic data, we achieve an increase in the classification accuracy for a dataset of intestine images.
Note: Treballs finals del Màster de Fonaments de Ciència de Dades, Facultat de matemàtiques, Universitat de Barcelona, Any: 2019, Tutor: Santi Seguí Mesquida
Appears in Collections:Màster Oficial - Fonaments de la Ciència de Dades
Programari - Treballs de l'alumnat

Files in This Item:
File Description SizeFormat 
overview.pdfoverview27.99 kBAdobe PDFView/Open
159485.pdfMemòria8.17 MBAdobe PDFView/Open
codi_font.zipCodi font43 MBzipView/Open

This item is licensed under a Creative Commons License Creative Commons