Domain transformation using semi-supervised CycleGAN for improving performance of classifying thyroid tissue images
Purpose A large number of research has been conducted on the classification of medical images using deep learning. The thyroid tissue images can be also classified by cancer types. Deep learning requires a large amount of data, but every medical institution cannot collect sufficient number of data f...
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Veröffentlicht in: | International journal for computer assisted radiology and surgery 2024-11, Vol.19 (11), p.2153-2163 |
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container_title | International journal for computer assisted radiology and surgery |
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creator | Ichiuji, Yoshihito Mabu, Shingo Hatta, Satomi Inai, Kunihiro Higuchi, Shohei Kido, Shoji |
description | Purpose
A large number of research has been conducted on the classification of medical images using deep learning. The thyroid tissue images can be also classified by cancer types. Deep learning requires a large amount of data, but every medical institution cannot collect sufficient number of data for deep learning. In that case, we can consider a case where a classifier trained at a certain medical institution that has a sufficient number of data is reused at other institutions. However, when using data from multiple institutions, it is necessary to unify the feature distribution because the feature of the data differs due to differences in data acquisition conditions.
Methods
To unify the feature distribution, the data from Institution T are transformed to have the closer distribution to that from Institution S by applying a domain transformation using semi-supervised CycleGAN. The proposed method enhances CycleGAN considering the feature distribution of classes for making appropriate domain transformation for classification. In addition, to address the problem of imbalanced data with different numbers of data for each cancer type, several methods dealing with imbalanced data are applied to semi-supervised CycleGAN.
Results
The experimental results showed that the classification performance was enhanced when the dataset from Institution S was used as training data and the testing dataset from Institution T was classified after applying domain transformation. In addition, focal loss contributed to improving the mean F1 score the best as a method that addresses the class imbalance.
Conclusion
The proposed method achieved the domain transformation of thyroid tissue images between two domains, where it retained the important features related to the classes across domains and showed the best F1 score with significant differences compared with other methods. In addition, the proposed method was further enhanced by addressing the class imbalance of the dataset. |
doi_str_mv | 10.1007/s11548-024-03061-x |
format | Article |
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A large number of research has been conducted on the classification of medical images using deep learning. The thyroid tissue images can be also classified by cancer types. Deep learning requires a large amount of data, but every medical institution cannot collect sufficient number of data for deep learning. In that case, we can consider a case where a classifier trained at a certain medical institution that has a sufficient number of data is reused at other institutions. However, when using data from multiple institutions, it is necessary to unify the feature distribution because the feature of the data differs due to differences in data acquisition conditions.
Methods
To unify the feature distribution, the data from Institution T are transformed to have the closer distribution to that from Institution S by applying a domain transformation using semi-supervised CycleGAN. The proposed method enhances CycleGAN considering the feature distribution of classes for making appropriate domain transformation for classification. In addition, to address the problem of imbalanced data with different numbers of data for each cancer type, several methods dealing with imbalanced data are applied to semi-supervised CycleGAN.
Results
The experimental results showed that the classification performance was enhanced when the dataset from Institution S was used as training data and the testing dataset from Institution T was classified after applying domain transformation. In addition, focal loss contributed to improving the mean F1 score the best as a method that addresses the class imbalance.
Conclusion
The proposed method achieved the domain transformation of thyroid tissue images between two domains, where it retained the important features related to the classes across domains and showed the best F1 score with significant differences compared with other methods. In addition, the proposed method was further enhanced by addressing the class imbalance of the dataset.</description><identifier>ISSN: 1861-6429</identifier><identifier>EISSN: 1861-6429</identifier><identifier>DOI: 10.1007/s11548-024-03061-x</identifier><identifier>PMID: 38238492</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Computer Imaging ; Computer Science ; Deep Learning ; Health Informatics ; Humans ; Image Interpretation, Computer-Assisted - methods ; Imaging ; Medicine ; Medicine & Public Health ; Original Article ; Pattern Recognition and Graphics ; Radiology ; Surgery ; Thyroid Gland - diagnostic imaging ; Thyroid Neoplasms - diagnostic imaging ; Thyroid Neoplasms - pathology ; Vision</subject><ispartof>International journal for computer assisted radiology and surgery, 2024-11, Vol.19 (11), p.2153-2163</ispartof><rights>CARS 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><rights>2024. CARS.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c298t-e421e413624f19ae284357a7b0c7d760c9319a48ef6ae91d02d885353026f53e3</cites><orcidid>0000-0002-8759-8337</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11548-024-03061-x$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11548-024-03061-x$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38238492$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ichiuji, Yoshihito</creatorcontrib><creatorcontrib>Mabu, Shingo</creatorcontrib><creatorcontrib>Hatta, Satomi</creatorcontrib><creatorcontrib>Inai, Kunihiro</creatorcontrib><creatorcontrib>Higuchi, Shohei</creatorcontrib><creatorcontrib>Kido, Shoji</creatorcontrib><title>Domain transformation using semi-supervised CycleGAN for improving performance of classifying thyroid tissue images</title><title>International journal for computer assisted radiology and surgery</title><addtitle>Int J CARS</addtitle><addtitle>Int J Comput Assist Radiol Surg</addtitle><description>Purpose
A large number of research has been conducted on the classification of medical images using deep learning. The thyroid tissue images can be also classified by cancer types. Deep learning requires a large amount of data, but every medical institution cannot collect sufficient number of data for deep learning. In that case, we can consider a case where a classifier trained at a certain medical institution that has a sufficient number of data is reused at other institutions. However, when using data from multiple institutions, it is necessary to unify the feature distribution because the feature of the data differs due to differences in data acquisition conditions.
Methods
To unify the feature distribution, the data from Institution T are transformed to have the closer distribution to that from Institution S by applying a domain transformation using semi-supervised CycleGAN. The proposed method enhances CycleGAN considering the feature distribution of classes for making appropriate domain transformation for classification. In addition, to address the problem of imbalanced data with different numbers of data for each cancer type, several methods dealing with imbalanced data are applied to semi-supervised CycleGAN.
Results
The experimental results showed that the classification performance was enhanced when the dataset from Institution S was used as training data and the testing dataset from Institution T was classified after applying domain transformation. In addition, focal loss contributed to improving the mean F1 score the best as a method that addresses the class imbalance.
Conclusion
The proposed method achieved the domain transformation of thyroid tissue images between two domains, where it retained the important features related to the classes across domains and showed the best F1 score with significant differences compared with other methods. In addition, the proposed method was further enhanced by addressing the class imbalance of the dataset.</description><subject>Computer Imaging</subject><subject>Computer Science</subject><subject>Deep Learning</subject><subject>Health Informatics</subject><subject>Humans</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>Imaging</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Original Article</subject><subject>Pattern Recognition and Graphics</subject><subject>Radiology</subject><subject>Surgery</subject><subject>Thyroid Gland - diagnostic imaging</subject><subject>Thyroid Neoplasms - diagnostic imaging</subject><subject>Thyroid Neoplasms - pathology</subject><subject>Vision</subject><issn>1861-6429</issn><issn>1861-6429</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kEtPwzAQhC0EgvL4AxyQj1wCfiWxj6g8pQoucLZMsilGSVy8SUX_PS4tiBMnrzzfjHaHkFPOLjhj5SVyniudMaEyJlnBs88dMuE6DYUSZvfPfEAOEd8ZU3kp831yILWQWhkxIXgdOud7OkTXYxNi5wYfejqi7-cUofMZjguIS49Q0-mqauHu6pEmkPpuEcNyjSX929lXQENDq9Yh-ma1loa3VQy-poNHHCF53BzwmOw1rkU42b5H5OX25nl6n82e7h6mV7OsEkYPGSjBQXFZCNVw40BoJfPSla-sKuuyYJWR6VtpaAoHhtdM1FrnMpdMFE0uQR6R801uWvRjBBxs57GCtnU9hBGtMMKwXBupEio2aBUDYoTGLmJaNq4sZ3Zdtt2UbVPZ9rts-5lMZ9v88bWD-tfy024C5AbAJPVziPY9jLFPN_8X-wUqz4y0</recordid><startdate>20241101</startdate><enddate>20241101</enddate><creator>Ichiuji, Yoshihito</creator><creator>Mabu, Shingo</creator><creator>Hatta, Satomi</creator><creator>Inai, Kunihiro</creator><creator>Higuchi, Shohei</creator><creator>Kido, Shoji</creator><general>Springer International Publishing</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-8759-8337</orcidid></search><sort><creationdate>20241101</creationdate><title>Domain transformation using semi-supervised CycleGAN for improving performance of classifying thyroid tissue images</title><author>Ichiuji, Yoshihito ; Mabu, Shingo ; Hatta, Satomi ; Inai, Kunihiro ; Higuchi, Shohei ; Kido, Shoji</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c298t-e421e413624f19ae284357a7b0c7d760c9319a48ef6ae91d02d885353026f53e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Computer Imaging</topic><topic>Computer Science</topic><topic>Deep Learning</topic><topic>Health Informatics</topic><topic>Humans</topic><topic>Image Interpretation, Computer-Assisted - methods</topic><topic>Imaging</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Original Article</topic><topic>Pattern Recognition and Graphics</topic><topic>Radiology</topic><topic>Surgery</topic><topic>Thyroid Gland - diagnostic imaging</topic><topic>Thyroid Neoplasms - diagnostic imaging</topic><topic>Thyroid Neoplasms - pathology</topic><topic>Vision</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ichiuji, Yoshihito</creatorcontrib><creatorcontrib>Mabu, Shingo</creatorcontrib><creatorcontrib>Hatta, Satomi</creatorcontrib><creatorcontrib>Inai, Kunihiro</creatorcontrib><creatorcontrib>Higuchi, Shohei</creatorcontrib><creatorcontrib>Kido, Shoji</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>International journal for computer assisted radiology and surgery</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ichiuji, Yoshihito</au><au>Mabu, Shingo</au><au>Hatta, Satomi</au><au>Inai, Kunihiro</au><au>Higuchi, Shohei</au><au>Kido, Shoji</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Domain transformation using semi-supervised CycleGAN for improving performance of classifying thyroid tissue images</atitle><jtitle>International journal for computer assisted radiology and surgery</jtitle><stitle>Int J CARS</stitle><addtitle>Int J Comput Assist Radiol Surg</addtitle><date>2024-11-01</date><risdate>2024</risdate><volume>19</volume><issue>11</issue><spage>2153</spage><epage>2163</epage><pages>2153-2163</pages><issn>1861-6429</issn><eissn>1861-6429</eissn><abstract>Purpose
A large number of research has been conducted on the classification of medical images using deep learning. The thyroid tissue images can be also classified by cancer types. Deep learning requires a large amount of data, but every medical institution cannot collect sufficient number of data for deep learning. In that case, we can consider a case where a classifier trained at a certain medical institution that has a sufficient number of data is reused at other institutions. However, when using data from multiple institutions, it is necessary to unify the feature distribution because the feature of the data differs due to differences in data acquisition conditions.
Methods
To unify the feature distribution, the data from Institution T are transformed to have the closer distribution to that from Institution S by applying a domain transformation using semi-supervised CycleGAN. The proposed method enhances CycleGAN considering the feature distribution of classes for making appropriate domain transformation for classification. In addition, to address the problem of imbalanced data with different numbers of data for each cancer type, several methods dealing with imbalanced data are applied to semi-supervised CycleGAN.
Results
The experimental results showed that the classification performance was enhanced when the dataset from Institution S was used as training data and the testing dataset from Institution T was classified after applying domain transformation. In addition, focal loss contributed to improving the mean F1 score the best as a method that addresses the class imbalance.
Conclusion
The proposed method achieved the domain transformation of thyroid tissue images between two domains, where it retained the important features related to the classes across domains and showed the best F1 score with significant differences compared with other methods. In addition, the proposed method was further enhanced by addressing the class imbalance of the dataset.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><pmid>38238492</pmid><doi>10.1007/s11548-024-03061-x</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0002-8759-8337</orcidid></addata></record> |
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subjects | Computer Imaging Computer Science Deep Learning Health Informatics Humans Image Interpretation, Computer-Assisted - methods Imaging Medicine Medicine & Public Health Original Article Pattern Recognition and Graphics Radiology Surgery Thyroid Gland - diagnostic imaging Thyroid Neoplasms - diagnostic imaging Thyroid Neoplasms - pathology Vision |
title | Domain transformation using semi-supervised CycleGAN for improving performance of classifying thyroid tissue images |
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