A generative adversarial network-based abnormality detection using only normal images for model training with application to digital breast tomosynthesis

Abstract Deep learning has shown tremendous potential in the task of object detection in images. However, a common challenge with this task is when only a limited number of images containing the object of interest are available. This is a particular issue in cancer screening, such as digital breast...

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Autores principales:	Albert Swiecicki, Nicholas Konz, Mateusz Buda, Maciej A. Mazurowski
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/508eb076572e45cab8b174526afb95f2
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id	oai:doaj.org-article:508eb076572e45cab8b174526afb95f2
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spelling	oai:doaj.org-article:508eb076572e45cab8b174526afb95f22021-12-02T15:54:50ZA generative adversarial network-based abnormality detection using only normal images for model training with application to digital breast tomosynthesis10.1038/s41598-021-89626-12045-2322https://doaj.org/article/508eb076572e45cab8b174526afb95f22021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-89626-1https://doaj.org/toc/2045-2322Abstract Deep learning has shown tremendous potential in the task of object detection in images. However, a common challenge with this task is when only a limited number of images containing the object of interest are available. This is a particular issue in cancer screening, such as digital breast tomosynthesis (DBT), where less than 1% of cases contain cancer. In this study, we propose a method to train an inpainting generative adversarial network to be used for cancer detection using only images that do not contain cancer. During inference, we removed a part of the image and used the network to complete the removed part. A significant error in completing an image part was considered an indication that such location is unexpected and thus abnormal. A large dataset of DBT images used in this study was collected at Duke University. It consisted of 19,230 reconstructed volumes from 4348 patients. Cancerous masses and architectural distortions were marked with bounding boxes by radiologists. Our experiments showed that the locations containing cancer were associated with a notably higher completion error than the non-cancer locations (mean error ratio of 2.77). All data used in this study has been made publicly available by the authors.Albert SwiecickiNicholas KonzMateusz BudaMaciej A. MazurowskiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Albert Swiecicki Nicholas Konz Mateusz Buda Maciej A. Mazurowski A generative adversarial network-based abnormality detection using only normal images for model training with application to digital breast tomosynthesis
description	Abstract Deep learning has shown tremendous potential in the task of object detection in images. However, a common challenge with this task is when only a limited number of images containing the object of interest are available. This is a particular issue in cancer screening, such as digital breast tomosynthesis (DBT), where less than 1% of cases contain cancer. In this study, we propose a method to train an inpainting generative adversarial network to be used for cancer detection using only images that do not contain cancer. During inference, we removed a part of the image and used the network to complete the removed part. A significant error in completing an image part was considered an indication that such location is unexpected and thus abnormal. A large dataset of DBT images used in this study was collected at Duke University. It consisted of 19,230 reconstructed volumes from 4348 patients. Cancerous masses and architectural distortions were marked with bounding boxes by radiologists. Our experiments showed that the locations containing cancer were associated with a notably higher completion error than the non-cancer locations (mean error ratio of 2.77). All data used in this study has been made publicly available by the authors.
format	article
author	Albert Swiecicki Nicholas Konz Mateusz Buda Maciej A. Mazurowski
author_facet	Albert Swiecicki Nicholas Konz Mateusz Buda Maciej A. Mazurowski
author_sort	Albert Swiecicki
title	A generative adversarial network-based abnormality detection using only normal images for model training with application to digital breast tomosynthesis
title_short	A generative adversarial network-based abnormality detection using only normal images for model training with application to digital breast tomosynthesis
title_full	A generative adversarial network-based abnormality detection using only normal images for model training with application to digital breast tomosynthesis
title_fullStr	A generative adversarial network-based abnormality detection using only normal images for model training with application to digital breast tomosynthesis
title_full_unstemmed	A generative adversarial network-based abnormality detection using only normal images for model training with application to digital breast tomosynthesis
title_sort	generative adversarial network-based abnormality detection using only normal images for model training with application to digital breast tomosynthesis
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/508eb076572e45cab8b174526afb95f2
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A generative adversarial network-based abnormality detection using only normal images for model training with application to digital breast tomosynthesis

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