학술논문
Is it real or not? Toward artificial intelligence-based realistic synthetic cytology image generation to augment teaching and quality assurance in pathology.
Document Type
Academic Journal
Author
McAlpine E; Department of Anatomical Pathology, National Health Laboratory Service, Johannesburg, South Africa; Division of Anatomical Pathology, School of Pathology, University of the Witwatersrand, Johannesburg, South Africa. Electronic address: ewen.mcalpine@wits.ac.za.; Michelow P; Department of Anatomical Pathology, National Health Laboratory Service, Johannesburg, South Africa; Division of Anatomical Pathology, School of Pathology, University of the Witwatersrand, Johannesburg, South Africa.; Liebenberg E; Division of Anatomical Pathology, School of Pathology, University of the Witwatersrand, Johannesburg, South Africa.; Celik T; School of Electrical and Information Engineering and Wits Institute of Data Science, University of the Witwatersrand, Johannesburg, South Africa.
Source
Publisher: Elsevier Country of Publication: United States NLM ID: 101613234 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2213-2945 (Print) Linking ISSN: 22132953 NLM ISO Abbreviation: J Am Soc Cytopathol Subsets: MEDLINE
Subject
Language
English
ISSN
2213-2945
Abstract
Introduction: Urine cytology offers a rapid and relatively inexpensive method to diagnose urothelial neoplasia. In our setting of a public sector laboratory in South Africa, urothelial neoplasia is rare, compromising pathology training in this specific aspect of cytology. Artificial intelligence-based synthetic image generation-specifically the use of generative adversarial networks (GANs)-offers a solution to this problem.
Materials and Methods: A limited, but morphologically diverse, dataset of 1000 malignant urothelial cytology images was used to train a StyleGAN3 model to create completely novel, synthetic examples of malignant urine cytology using computer resources within reach of most pathology departments worldwide.
Results: We have presented the results of our trained GAN model, which was able to generate realistic, morphologically diverse examples of malignant urine cytology images when trained using a modest dataset. Although the trained model is capable of generating realistic images, we have also presented examples for which unrealistic and artifactual images were generated-illustrating the need for manual curation when using this technology in a training context.
Conclusions: We have presented a proof-of-concept illustration of creating synthetic malignant urine cytology images using machine learning technology to augment cytology training when real-world examples are sparse. We have shown that despite significant morphologic diversity in terms of staining variations, slide background, variations in the diagnostic malignant cellular elements, the presence of other nondiagnostic cellular elements, and artifacts, visually acceptable and varied results are achievable using limited data and computing resources.
(Copyright © 2022 American Society of Cytopathology. Published by Elsevier Inc. All rights reserved.)
Materials and Methods: A limited, but morphologically diverse, dataset of 1000 malignant urothelial cytology images was used to train a StyleGAN3 model to create completely novel, synthetic examples of malignant urine cytology using computer resources within reach of most pathology departments worldwide.
Results: We have presented the results of our trained GAN model, which was able to generate realistic, morphologically diverse examples of malignant urine cytology images when trained using a modest dataset. Although the trained model is capable of generating realistic images, we have also presented examples for which unrealistic and artifactual images were generated-illustrating the need for manual curation when using this technology in a training context.
Conclusions: We have presented a proof-of-concept illustration of creating synthetic malignant urine cytology images using machine learning technology to augment cytology training when real-world examples are sparse. We have shown that despite significant morphologic diversity in terms of staining variations, slide background, variations in the diagnostic malignant cellular elements, the presence of other nondiagnostic cellular elements, and artifacts, visually acceptable and varied results are achievable using limited data and computing resources.
(Copyright © 2022 American Society of Cytopathology. Published by Elsevier Inc. All rights reserved.)