Histopathological Image Retrieval Based on Asymmetric Residual Hash and DNA Coding

Histopathological image retrieval is a key technology for computer-aided diagnosis. However, patients are reluctant to reveal their privacy in histopathological image retrieval. In order to further improve the effectiveness and safety of histopathological image retrieval, this paper proposes a new h...

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Autores principales:	Shuli Cheng, Liejun Wang, Anyu Du
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2019
Materias:	Histopathological image retrieval asymmetric residual hash computer-aided diagnosis patient privacy DNA coding Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/2f4f4c11659144cd9e284804ab10bcd7
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id	oai:doaj.org-article:2f4f4c11659144cd9e284804ab10bcd7
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spelling	oai:doaj.org-article:2f4f4c11659144cd9e284804ab10bcd72021-11-19T00:03:06ZHistopathological Image Retrieval Based on Asymmetric Residual Hash and DNA Coding2169-353610.1109/ACCESS.2019.2930177https://doaj.org/article/2f4f4c11659144cd9e284804ab10bcd72019-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/8767924/https://doaj.org/toc/2169-3536Histopathological image retrieval is a key technology for computer-aided diagnosis. However, patients are reluctant to reveal their privacy in histopathological image retrieval. In order to further improve the effectiveness and safety of histopathological image retrieval, this paper proposes a new histopathological retrieval scheme based on asymmetric residual hash (ARH) and DNA coding techniques. In this paper, we first present a novel ARH for histopathological image retrieval to improve the effectiveness of histopathological search scheme, and then we use the 5-D hyperchaotic system to protect patient privacy. Specifically, the contribution consists of four aspects: 1) A histopathology ciphertext domain search scheme was proposed to improve the performance of computer-aided diagnosis. 2) An asymmetric approach was implemented to process histopathological query points and database points, and a novel asymmetric residual hash algorithm was first proposed to improve the accuracy and speed of histopathological image retrieval. 3) The 5-D hyperchaotic system and DNA coding technique are applied to histopathological image retrieval to protect patient privacy. 4) The loss function is constructed and optimized to learn network parameters and hash codes. The simulation experiment was performed on three datasets (Kimia Path24, Kimia Path960, and Malaria), and the results proved the effectiveness of the ARH algorithm. In addition, our proposed search method can resist common types of attacks during histopathological data transmission. Specifically, the MAP of the ARH is 0.9678 on the KIMIA Path24 with the value of hyperparameter is 125 and the length of hash code is 32. The MAP of the ARH is 0.966 on the KIMIA Path960 with the value of hyperparameter is 225 and the length of hash code is 32. The MAP of the ARH is 0.9482 on the Malaria with the value of hyperparameter is 10 and the length of hash code is 24.Shuli ChengLiejun WangAnyu DuIEEEarticleHistopathological image retrievalasymmetric residual hashcomputer-aided diagnosispatient privacyDNA codingElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 7, Pp 101388-101400 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Histopathological image retrieval asymmetric residual hash computer-aided diagnosis patient privacy DNA coding Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Histopathological image retrieval asymmetric residual hash computer-aided diagnosis patient privacy DNA coding Electrical engineering. Electronics. Nuclear engineering TK1-9971 Shuli Cheng Liejun Wang Anyu Du Histopathological Image Retrieval Based on Asymmetric Residual Hash and DNA Coding
description	Histopathological image retrieval is a key technology for computer-aided diagnosis. However, patients are reluctant to reveal their privacy in histopathological image retrieval. In order to further improve the effectiveness and safety of histopathological image retrieval, this paper proposes a new histopathological retrieval scheme based on asymmetric residual hash (ARH) and DNA coding techniques. In this paper, we first present a novel ARH for histopathological image retrieval to improve the effectiveness of histopathological search scheme, and then we use the 5-D hyperchaotic system to protect patient privacy. Specifically, the contribution consists of four aspects: 1) A histopathology ciphertext domain search scheme was proposed to improve the performance of computer-aided diagnosis. 2) An asymmetric approach was implemented to process histopathological query points and database points, and a novel asymmetric residual hash algorithm was first proposed to improve the accuracy and speed of histopathological image retrieval. 3) The 5-D hyperchaotic system and DNA coding technique are applied to histopathological image retrieval to protect patient privacy. 4) The loss function is constructed and optimized to learn network parameters and hash codes. The simulation experiment was performed on three datasets (Kimia Path24, Kimia Path960, and Malaria), and the results proved the effectiveness of the ARH algorithm. In addition, our proposed search method can resist common types of attacks during histopathological data transmission. Specifically, the MAP of the ARH is 0.9678 on the KIMIA Path24 with the value of hyperparameter is 125 and the length of hash code is 32. The MAP of the ARH is 0.966 on the KIMIA Path960 with the value of hyperparameter is 225 and the length of hash code is 32. The MAP of the ARH is 0.9482 on the Malaria with the value of hyperparameter is 10 and the length of hash code is 24.
format	article
author	Shuli Cheng Liejun Wang Anyu Du
author_facet	Shuli Cheng Liejun Wang Anyu Du
author_sort	Shuli Cheng
title	Histopathological Image Retrieval Based on Asymmetric Residual Hash and DNA Coding
title_short	Histopathological Image Retrieval Based on Asymmetric Residual Hash and DNA Coding
title_full	Histopathological Image Retrieval Based on Asymmetric Residual Hash and DNA Coding
title_fullStr	Histopathological Image Retrieval Based on Asymmetric Residual Hash and DNA Coding
title_full_unstemmed	Histopathological Image Retrieval Based on Asymmetric Residual Hash and DNA Coding
title_sort	histopathological image retrieval based on asymmetric residual hash and dna coding
publisher	IEEE
publishDate	2019
url	https://doaj.org/article/2f4f4c11659144cd9e284804ab10bcd7
work_keys_str_mv	AT shulicheng histopathologicalimageretrievalbasedonasymmetricresidualhashanddnacoding AT liejunwang histopathologicalimageretrievalbasedonasymmetricresidualhashanddnacoding AT anyudu histopathologicalimageretrievalbasedonasymmetricresidualhashanddnacoding
_version_	1718420660322041856

Histopathological Image Retrieval Based on Asymmetric Residual Hash and DNA Coding

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