Navid Shekarchizadeh

About Navid Shekarchizadeh:
No description specified

LHA ID: 8K64E2CMHV-5

Location:  Germany

ORCID: Not specified

Joined: 7th Sep 2023

Expertise: Not specified

Tools: Not specified

project_administratorprogramme_administrator
help Their tags

This person has not yet tagged anything.

Related items

Prediction model of Length of Stay and probability of rehospitalization
No description specified

Programme: Medical Data Science

Public web page: Not specified

Synthetica: generation and evaluation of synthetic data

Generating and evaluating synthetic data in the medical domain. The synthetic data could be tabular or image.

Programme: Medical Data Science

Public web page: Not specified

GAN-Based Approaches for Generating Structured Data in the Medical Domain
Synthetica: generation and evaluation of synthetic data

Abstract (Expand)

Modern machine and deep learning methods require large datasets to achieve reliable and robust results. This requirement is often difficult to meet in the medical field, due to data sharing limitations …ng limitations imposed by privacy regulations or the presence of a small number of patients (e.g., rare diseases). To address this data scarcity and to improve the situation, novel generative models such as Generative Adversarial Networks (GANs) have been widely used to generate synthetic data that mimic real data by representing features that reflect health-related information without reference to real patients. In this paper, we consider several GAN models to generate synthetic data used for training binary (malignant/benign) classifiers, and compare their performances in terms of classification accuracy with cases where only real data are considered. We aim to investigate how synthetic data can improve classification accuracy, especially when a small amount of data is available. To this end, we have developed and implemented an evaluation framework where binary classifiers are trained on extended datasets containing both real and synthetic data. The results show improved accuracy for classifiers trained with generated data from more advanced GAN models, even when limited amounts of original data are available.

Authors: Masoud Abedi, Lars Hempel, Sina Sadeghi, Toralf Kirsten

Date Published: 1st Jul 2022

Publication Type: Journal article

Medical Data Science

​Welcome to the Department for Medical Data Sciences at the University Medical Center in Leipzig. We are an interdisciplinary team focusing on medical data sciences in order to increase the value for the patients. In this way, we are mainly working with medical data. Our focus is twofold. On the one hand, we are working on different facets of computing infrastructures allowing us to prepare and analyze medical data, such as for distributed computing. Secondly, we focus on the analysis of medical ...

Projects: Synthetica: generation and evaluation of synthetic data, Prediction model of Length of Stay and probability of rehospitalization, LEUKO-EXPERT, LeukoExpert

Web page: https://www.uniklinikum-leipzig.de/einrichtungen/medical-data-science/en/team

Navigate

Health Atlas - Local Data Hub/Leipzig

The Health Atlas - Local Data Hub/Leipzig is an alliance of medical ontologists, medical systems biologists and clinical trials groups to design and implement a multi-functional and quality-assured atlas. It provides models, data and metadata on specific use cases from medical research fields.

Registered Repository

re3data.org Badge
Powered by
 (v.1.13.0-master)
Health Atlas | Funding and Programmes | Credits | Terms & Conditions | Privacy Policy | Imprint
Copyright © 2008 - 2021 The University of Manchester and HITS gGmbH
Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig

By continuing to use this site you agree to the use of cookies