Generative Adversarial Networks for Anomaly Detection in Biomedical Imaging: A Study on Seven Medical Image Datasets-Reference-Cited by-同舟云学术

Generative Adversarial Networks for Anomaly Detection in Biomedical Imaging: A Study on Seven Medical Image Datasets

Published:2023 Issue: Volume:11 Page:17906-17921
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Esmaeili Marzieh¹^ORCID,Toosi Amirhosein²,Roshanpoor Arash³,Changizi Vahid⁴,Ghazisaeedi Marjan¹^ORCID,Rahmim Arman²^ORCID,Sabokrou Mohammad⁵^ORCID

Affiliation:

1. Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran

2. Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada

3. Department of Computer, Yadegar-e-Imam Khomeini, Janat-Abad Branch, Islamic Azad University, Tehran, Iran

4. Department of Radiology and Radiotherapy Technology, School of Allied Health Sciences, Tehran University of Medical Sciences, Tehran, Iran

5. School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran

Funder

Tehran University of Medical Sciences (TUMS), as part of a Ph.D. dissertation from TUMS

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10043696.pdf?arnumber=10043696

Reference84 articles.

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2. Alice: Towards understanding adversarial learning for joint distribution matching;li;Proc 31st Int Conf Neural Inf Process Syst,2017

3. Artificial intelligence in radiology

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