Artificial Intelligence-Based Semisupervised Self-Training Algorithm in Pathological Tissue Image Segmentation
In the field of medical image processing, due to the differences in tissues, organs, and imaging methods, obtained medical images have significant differences. With the development of intelligence in medicine, an increasing number of computing optimization algorithms based on AI technology have also...
Uložené v:
| Vydané v: | Computational intelligence and neuroscience Ročník 2022; s. 1 - 12 |
|---|---|
| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
United States
Hindawi
13.06.2022
John Wiley & Sons, Inc |
| Predmet: | |
| ISSN: | 1687-5265, 1687-5273, 1687-5273 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | In the field of medical image processing, due to the differences in tissues, organs, and imaging methods, obtained medical images have significant differences. With the development of intelligence in medicine, an increasing number of computing optimization algorithms based on AI technology have also been applied to the field of medicine. Because the image segmentation algorithm based on the semisupervised self-training algorithm solves initialization class center large randomness problem in the traditional cluster-based image segmentation algorithm, this article aims to integrate the artificial intelligence semisupervised self-training algorithm into the pathological tissue image segmentation problem. An experimental group is designed to collect sample images and the algorithm proposed in this article is used to perform image segmentation to achieve a better visual experience and images. Although there is no general image segmentation theory, many scholars have been committed to applying new concepts and new methods to image segmentation in recent years and combining specific theoretical image segmentation methods has achieved good application results in image segmentation. For example, wavelet analysis, wavelet transform, neural networks, and genetic algorithms can effectively improve the segmentation effect. The results of the Seg cutting method designed in this article show that, in retinal blood vessel segmentation results on a database of healthy people, the sensitivity value is 0.941633, the false-positive rate is 0.952933, the specificity is 0.956787, and the accuracy rate is 0.96182, which are all higher than those in other methods. Image cutting methods such as FNN, CNN, and AWN have addressed the case tissue image cutting problem. Using the Seg cutting method designed in this article to segment the retinal blood vessels on a diabetes patient database, the sensitivity value is 0.8106, the false-positive rate is 0.0511, the specificity is 0.9712, the accuracy is 0.9421, and the false-positive rate is omitted. The false-positive rate is lower than AWN, and other indicators are higher than FNN, CNN, AWN, and other image cutting methods. The application of artificial intelligence-based semisupervised self-training algorithms in pathological tissue image segmentation is realized. |
|---|---|
| AbstractList | In the field of medical image processing, due to the differences in tissues, organs, and imaging methods, obtained medical images have significant differences. With the development of intelligence in medicine, an increasing number of computing optimization algorithms based on AI technology have also been applied to the field of medicine. Because the image segmentation algorithm based on the semisupervised self-training algorithm solves initialization class center large randomness problem in the traditional cluster-based image segmentation algorithm, this article aims to integrate the artificial intelligence semisupervised self-training algorithm into the pathological tissue image segmentation problem. An experimental group is designed to collect sample images and the algorithm proposed in this article is used to perform image segmentation to achieve a better visual experience and images. Although there is no general image segmentation theory, many scholars have been committed to applying new concepts and new methods to image segmentation in recent years and combining specific theoretical image segmentation methods has achieved good application results in image segmentation. For example, wavelet analysis, wavelet transform, neural networks, and genetic algorithms can effectively improve the segmentation effect. The results of the Seg cutting method designed in this article show that, in retinal blood vessel segmentation results on a database of healthy people, the sensitivity value is 0.941633, the false-positive rate is 0.952933, the specificity is 0.956787, and the accuracy rate is 0.96182, which are all higher than those in other methods. Image cutting methods such as FNN, CNN, and AWN have addressed the case tissue image cutting problem. Using the Seg cutting method designed in this article to segment the retinal blood vessels on a diabetes patient database, the sensitivity value is 0.8106, the false-positive rate is 0.0511, the specificity is 0.9712, the accuracy is 0.9421, and the false-positive rate is omitted. The false-positive rate is lower than AWN, and other indicators are higher than FNN, CNN, AWN, and other image cutting methods. The application of artificial intelligence-based semisupervised self-training algorithms in pathological tissue image segmentation is realized. In the field of medical image processing, due to the differences in tissues, organs, and imaging methods, obtained medical images have significant differences. With the development of intelligence in medicine, an increasing number of computing optimization algorithms based on AI technology have also been applied to the field of medicine. Because the image segmentation algorithm based on the semisupervised self-training algorithm solves initialization class center large randomness problem in the traditional cluster-based image segmentation algorithm, this article aims to integrate the artificial intelligence semisupervised self-training algorithm into the pathological tissue image segmentation problem. An experimental group is designed to collect sample images and the algorithm proposed in this article is used to perform image segmentation to achieve a better visual experience and images. Although there is no general image segmentation theory, many scholars have been committed to applying new concepts and new methods to image segmentation in recent years and combining specific theoretical image segmentation methods has achieved good application results in image segmentation. For example, wavelet analysis, wavelet transform, neural networks, and genetic algorithms can effectively improve the segmentation effect. The results of the Seg cutting method designed in this article show that, in retinal blood vessel segmentation results on a database of healthy people, the sensitivity value is 0.941633, the false-positive rate is 0.952933, the specificity is 0.956787, and the accuracy rate is 0.96182, which are all higher than those in other methods. Image cutting methods such as FNN, CNN, and AWN have addressed the case tissue image cutting problem. Using the Seg cutting method designed in this article to segment the retinal blood vessels on a diabetes patient database, the sensitivity value is 0.8106, the false-positive rate is 0.0511, the specificity is 0.9712, the accuracy is 0.9421, and the false-positive rate is omitted. The false-positive rate is lower than AWN, and other indicators are higher than FNN, CNN, AWN, and other image cutting methods. The application of artificial intelligence-based semisupervised self-training algorithms in pathological tissue image segmentation is realized.In the field of medical image processing, due to the differences in tissues, organs, and imaging methods, obtained medical images have significant differences. With the development of intelligence in medicine, an increasing number of computing optimization algorithms based on AI technology have also been applied to the field of medicine. Because the image segmentation algorithm based on the semisupervised self-training algorithm solves initialization class center large randomness problem in the traditional cluster-based image segmentation algorithm, this article aims to integrate the artificial intelligence semisupervised self-training algorithm into the pathological tissue image segmentation problem. An experimental group is designed to collect sample images and the algorithm proposed in this article is used to perform image segmentation to achieve a better visual experience and images. Although there is no general image segmentation theory, many scholars have been committed to applying new concepts and new methods to image segmentation in recent years and combining specific theoretical image segmentation methods has achieved good application results in image segmentation. For example, wavelet analysis, wavelet transform, neural networks, and genetic algorithms can effectively improve the segmentation effect. The results of the Seg cutting method designed in this article show that, in retinal blood vessel segmentation results on a database of healthy people, the sensitivity value is 0.941633, the false-positive rate is 0.952933, the specificity is 0.956787, and the accuracy rate is 0.96182, which are all higher than those in other methods. Image cutting methods such as FNN, CNN, and AWN have addressed the case tissue image cutting problem. Using the Seg cutting method designed in this article to segment the retinal blood vessels on a diabetes patient database, the sensitivity value is 0.8106, the false-positive rate is 0.0511, the specificity is 0.9712, the accuracy is 0.9421, and the false-positive rate is omitted. The false-positive rate is lower than AWN, and other indicators are higher than FNN, CNN, AWN, and other image cutting methods. The application of artificial intelligence-based semisupervised self-training algorithms in pathological tissue image segmentation is realized. |
| Audience | Academic |
| Author | Liu, Linlin Li, Qun |
| AuthorAffiliation | 1 School of Electronic Information Engineering, Ningbo Polytechnic, Ningbo 315800, China 2 School of Information and Engineering, China Jiliang University, Hangzhou 310000, Zhejiang, China |
| AuthorAffiliation_xml | – name: 2 School of Information and Engineering, China Jiliang University, Hangzhou 310000, Zhejiang, China – name: 1 School of Electronic Information Engineering, Ningbo Polytechnic, Ningbo 315800, China |
| Author_xml | – sequence: 1 givenname: Qun surname: Li fullname: Li, Qun organization: School of Electronic Information EngineeringNingbo PolytechnicNingbo 315800China – sequence: 2 givenname: Linlin orcidid: 0000-0003-3970-6396 surname: Liu fullname: Liu, Linlin organization: School of Information and EngineeringChina Jiliang UniversityHangzhou 310000ZhejiangChinacjlu.edu.cn |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35733571$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9ktuL1DAUxousuBd981kKvghaN5emaV4WxsXLwIKC43NIk7RzljQZk3bF_96MHUdd0IeQhPzO952cc86LEx-8LYqnGL3GmLFLggi5pAwhJsiD4gw3La8Y4fTkeG7YaXGe0m1GOEPkUXFKGad54bPCr-IEPWhQrlz7yToHg_XaVm9Usqb8bEdI887GO1iurq82UYEHP5QrN4QI03YswZef1LQNLgygs9IGUpptuR7VYHPQMFo_qQmCf1w87JVL9slhvyi-vHu7uf5Q3Xx8v75e3VS6Rs1UWWOEMLrpmEad6HDHue5IzYVBlIi-4boWrEe2Q31Pue0M5jUTpre6rjGlhl4UV4vubu5Ga3T2j8rJXYRRxe8yKJB_v3jYyiHcSUFQKxqSBV4cBGL4Ots0yVwIncujvA1zkqRpEaGsRTijz--ht2GOPn_vJyUwQ5T9pgblrATfh-yr96JyxVFLa07bNlPP_sz7mPCvhmXg1QLoGFKKtj8iGMn9PMj9PMjDPGSc3MM1LJ3I7uD-FfRyCdqCN-ob_N_iB-0nxSU |
| CitedBy_id | crossref_primary_10_1155_2022_5986283 crossref_primary_10_1155_2023_9753618 |
| Cites_doi | 10.1126/science.aam6960 10.1109/access.2019.2950122 10.1186/s13040-017-0147-3 10.1007/s13042-015-0328-7 10.1155/2021/4060686 10.1016/j.cmpb.2016.01.016 10.1016/j.jacr.2017.12.026 10.1007/s00521-021-05793-2 10.1007/s11036-017-0932-8 10.1016/j.ijleo.2013.10.043 10.1016/j.media.2013.03.001 10.1007/s13369-013-0559-4 10.2991/jrnal.2016.3.1.6 10.1001/jama.2017.14580 10.1001/jama.2016.17438 10.1007/s00521-021-05878-y 10.2200/s00692ed1v01y201601aim032 10.1002/rwm3.20554 10.1186/s12711-016-0262-5 10.1038/538291a 10.1002/pmic.200800936 10.1007/s12652-020-02665-w 10.1007/s12652-020-02451-8 10.1016/j.neuron.2017.06.011 10.1016/j.neuroimage.2012.07.048 |
| ContentType | Journal Article |
| Copyright | Copyright © 2022 Qun Li and Linlin Liu. COPYRIGHT 2022 John Wiley & Sons, Inc. Copyright © 2022 Qun Li and Linlin Liu. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 Copyright © 2022 Qun Li and Linlin Liu. 2022 |
| Copyright_xml | – notice: Copyright © 2022 Qun Li and Linlin Liu. – notice: COPYRIGHT 2022 John Wiley & Sons, Inc. – notice: Copyright © 2022 Qun Li and Linlin Liu. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 – notice: Copyright © 2022 Qun Li and Linlin Liu. 2022 |
| DBID | RHU RHW RHX AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7QF 7QQ 7SC 7SE 7SP 7SR 7TA 7TB 7TK 7U5 7X7 7XB 8AL 8BQ 8FD 8FE 8FG 8FH 8FI 8FJ 8FK ABJCF ABUWG AFKRA ARAPS AZQEC BBNVY BENPR BGLVJ BHPHI CCPQU CWDGH DWQXO F28 FR3 FYUFA GHDGH GNUQQ H8D H8G HCIFZ JG9 JQ2 K7- K9. KR7 L6V L7M LK8 L~C L~D M0N M0S M1P M7P M7S P5Z P62 PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS PSYQQ PTHSS Q9U 7X8 5PM |
| DOI | 10.1155/2022/3500592 |
| DatabaseName | Hindawi Publishing Complete Hindawi Publishing Subscription Journals Hindawi Publishing Open Access CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Aluminium Industry Abstracts Ceramic Abstracts Computer and Information Systems Abstracts Corrosion Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts Materials Business File Mechanical & Transportation Engineering Abstracts Neurosciences Abstracts Solid State and Superconductivity Abstracts Health & Medical Collection ProQuest Central (purchase pre-March 2016) Computing Database (Alumni Edition) METADEX Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials Biological Science Collection ProQuest Central ProQuest Technology Collection Natural Science Collection ProQuest One Middle East & Africa Database ProQuest Central ANTE: Abstracts in New Technology & Engineering Engineering Research Database Proquest Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student Aerospace Database Copper Technical Reference Library SciTech Premium Collection Materials Research Database ProQuest Computer Science Collection Computer Science Database ProQuest Health & Medical Complete (Alumni) Civil Engineering Abstracts ProQuest Engineering Collection Advanced Technologies Database with Aerospace Biological Sciences Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Computing Database ProQuest Health & Medical Collection PML(ProQuest Medical Library) Proquest-Biological Science Engineering Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic ProQuest Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China ProQuest One Psychology Engineering Collection ProQuest Central Basic MEDLINE - Academic PubMed Central (Full Participant titles) |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database Materials Research Database ProQuest One Psychology Computer Science Database ProQuest Central Student ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts SciTech Premium Collection ProQuest Central China Materials Business File ProQuest One Applied & Life Sciences Engineered Materials Abstracts Health Research Premium Collection Natural Science Collection Health & Medical Research Collection Biological Science Collection ProQuest Central (New) Engineering Collection ANTE: Abstracts in New Technology & Engineering Advanced Technologies & Aerospace Collection Engineering Database Aluminium Industry Abstracts ProQuest Biological Science Collection ProQuest One Academic Eastern Edition Electronics & Communications Abstracts ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Ceramic Abstracts Biological Science Database Neurosciences Abstracts ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest One Academic UKI Edition Solid State and Superconductivity Abstracts Engineering Research Database ProQuest One Academic ProQuest One Academic (New) Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) Mechanical & Transportation Engineering Abstracts ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Central Aerospace Database Copper Technical Reference Library ProQuest Health & Medical Research Collection ProQuest Engineering Collection Middle East & Africa Database Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Advanced Technologies Database with Aerospace Civil Engineering Abstracts ProQuest Computing ProQuest Central Basic ProQuest Computing (Alumni Edition) ProQuest SciTech Collection METADEX Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database ProQuest Medical Library Materials Science & Engineering Collection Corrosion Abstracts ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | Publicly Available Content Database CrossRef MEDLINE MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: RHX name: Hindawi Publishing Open Access url: http://www.hindawi.com/journals/ sourceTypes: Publisher – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: ProQuest Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Anatomy & Physiology |
| EISSN | 1687-5273 |
| Editor | Sharma, Kapil |
| Editor_xml | – sequence: 1 givenname: Kapil surname: Sharma fullname: Sharma, Kapil |
| EndPage | 12 |
| ExternalDocumentID | PMC9208962 A708347388 35733571 10_1155_2022_3500592 |
| Genre | Retracted Publication Journal Article |
| GroupedDBID | --- 188 29F 2WC 3V. 4.4 53G 5GY 5VS 6J9 7X7 8FE 8FG 8FH 8FI 8FJ 8R4 8R5 AAFWJ AAJEY AAKPC ABDBF ABIVO ABJCF ABUWG ACGFO ACIWK ACM ACPRK ADBBV ADRAZ AENEX AFKRA AHMBA AINHJ ALMA_UNASSIGNED_HOLDINGS AOIJS ARAPS AZQEC BAWUL BBNVY BCNDV BENPR BGLVJ BHPHI BPHCQ BVXVI CCPQU CS3 CWDGH DIK DWQXO E3Z EBD EBS EMOBN ESX F5P FYUFA GNUQQ GROUPED_DOAJ GX1 HCIFZ HMCUK HYE I-F IAO ICD INH INR IPY ITC K6V K7- KQ8 L6V LK8 M0N M1P M48 M7P M7S MK~ O5R O5S OK1 P2P P62 PIMPY PQQKQ PROAC PSQYO PSYQQ PTHSS Q2X RHU RHW RHX RNS RPM SV3 TR2 TUS UKHRP XH6 ~8M 0R~ 24P AAMMB AAYXX ACCMX ACUHS AEFGJ AFFHD AGXDD AIDQK AIDYY ALUQN CITATION H13 IHR OVT PGMZT PHGZM PHGZT PJZUB PPXIY PQGLB 2UF C1A CGR CNMHZ CUY CVCKV CVF ECM EIF EJD IL9 NPM UZ4 7QF 7QQ 7SC 7SE 7SP 7SR 7TA 7TB 7TK 7U5 7XB 8AL 8BQ 8FD 8FK F28 FR3 H8D H8G JG9 JQ2 K9. KR7 L7M L~C L~D PKEHL PQEST PQUKI PRINS Q9U 7X8 PUEGO 5PM |
| ID | FETCH-LOGICAL-c406t-edd99dc6b5c0b9b1b77cb2479d0329f67c495f0eb0ff37ebd17459dfec44133d3 |
| IEDL.DBID | P5Z |
| ISICitedReferencesCount | 5 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000823736700019&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1687-5265 1687-5273 |
| IngestDate | Tue Nov 04 02:00:30 EST 2025 Sun Sep 28 09:31:30 EDT 2025 Sat Nov 29 14:58:51 EST 2025 Tue Nov 11 10:56:23 EST 2025 Wed Feb 19 02:22:32 EST 2025 Sat Nov 29 02:55:55 EST 2025 Tue Nov 18 21:29:49 EST 2025 Sun Jun 02 18:51:49 EDT 2024 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Language | English |
| License | This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. https://creativecommons.org/licenses/by/4.0 Copyright © 2022 Qun Li and Linlin Liu. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c406t-edd99dc6b5c0b9b1b77cb2479d0329f67c495f0eb0ff37ebd17459dfec44133d3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 ObjectType-Correction/Retraction-3 Academic Editor: Kapil Sharma |
| ORCID | 0000-0003-3970-6396 |
| OpenAccessLink | https://www.proquest.com/docview/2680915035?pq-origsite=%requestingapplication% |
| PMID | 35733571 |
| PQID | 2680915035 |
| PQPubID | 237303 |
| PageCount | 12 |
| ParticipantIDs | pubmedcentral_primary_oai_pubmedcentral_nih_gov_9208962 proquest_miscellaneous_2680235801 proquest_journals_2680915035 gale_infotracmisc_A708347388 pubmed_primary_35733571 crossref_primary_10_1155_2022_3500592 crossref_citationtrail_10_1155_2022_3500592 hindawi_primary_10_1155_2022_3500592 |
| PublicationCentury | 2000 |
| PublicationDate | 2022-06-13 |
| PublicationDateYYYYMMDD | 2022-06-13 |
| PublicationDate_xml | – month: 06 year: 2022 text: 2022-06-13 day: 13 |
| PublicationDecade | 2020 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States – name: New York |
| PublicationTitle | Computational intelligence and neuroscience |
| PublicationTitleAlternate | Comput Intell Neurosci |
| PublicationYear | 2022 |
| Publisher | Hindawi John Wiley & Sons, Inc |
| Publisher_xml | – name: Hindawi – name: John Wiley & Sons, Inc |
| References | A. S. Nair (22) 2013; 9 25 26 10 11 12 13 14 15 16 17 18 19 C. Decaestecker (24) 2010; 9 1 2 3 4 5 6 7 8 9 20 21 M. C. Yiannakas (23) 2012; 63 37564518 - Comput Intell Neurosci. 2023 Aug 2;2023:9753618 |
| References_xml | – ident: 8 doi: 10.1126/science.aam6960 – ident: 18 doi: 10.1109/access.2019.2950122 – ident: 4 doi: 10.1186/s13040-017-0147-3 – ident: 13 doi: 10.1007/s13042-015-0328-7 – ident: 26 doi: 10.1155/2021/4060686 – ident: 12 doi: 10.1016/j.cmpb.2016.01.016 – ident: 3 doi: 10.1016/j.jacr.2017.12.026 – ident: 19 doi: 10.1007/s00521-021-05793-2 – ident: 7 doi: 10.1007/s11036-017-0932-8 – ident: 11 doi: 10.1016/j.ijleo.2013.10.043 – ident: 25 doi: 10.1016/j.media.2013.03.001 – ident: 21 doi: 10.1007/s13369-013-0559-4 – ident: 15 doi: 10.2991/jrnal.2016.3.1.6 – ident: 5 doi: 10.1001/jama.2017.14580 – ident: 9 doi: 10.1001/jama.2016.17438 – ident: 14 doi: 10.1007/s00521-021-05878-y – ident: 10 doi: 10.2200/s00692ed1v01y201601aim032 – ident: 1 doi: 10.1002/rwm3.20554 – ident: 17 doi: 10.1186/s12711-016-0262-5 – ident: 2 doi: 10.1038/538291a – volume: 9 start-page: 4478 issue: 19 year: 2010 ident: 24 article-title: Requirements for the valid quantification of immunostains on tissue microarray materials using image analysis publication-title: Proteomics doi: 10.1002/pmic.200800936 – ident: 20 doi: 10.1007/s12652-020-02665-w – ident: 16 doi: 10.1007/s12652-020-02451-8 – volume: 9 start-page: 63 issue: 2 year: 2013 ident: 22 article-title: An unsupervised hybrid ga approach for identifying brain pathological tissue in mri images publication-title: International Journal of Computational Intelligence Research – ident: 6 doi: 10.1016/j.neuron.2017.06.011 – volume: 63 start-page: 1054 issue: 3 year: 2012 ident: 23 article-title: Feasibility of grey matter and white matter segmentation of the upper cervical cord in vivo: a pilot study with application to magnetisation transfer measurement publication-title: NeuroImage doi: 10.1016/j.neuroimage.2012.07.048 – reference: 37564518 - Comput Intell Neurosci. 2023 Aug 2;2023:9753618 |
| SSID | ssj0057502 |
| Score | 2.2978764 |
| SecondaryResourceType | retracted_publication |
| Snippet | In the field of medical image processing, due to the differences in tissues, organs, and imaging methods, obtained medical images have significant differences.... |
| SourceID | pubmedcentral proquest gale pubmed crossref hindawi |
| SourceType | Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 1 |
| SubjectTerms | Active learning Algorithms Artificial Intelligence Blood vessels Clustering Consciousness Cutting Data processing Diabetes mellitus Genetic algorithms Humans Image processing Image Processing, Computer-Assisted - methods Image segmentation Machine learning Mathematical optimization Medical imaging Medical imaging equipment Neural networks Neural Networks, Computer Optimization Organs Retina Sensitivity Wavelet Analysis Wavelet transforms |
| SummonAdditionalLinks | – databaseName: Hindawi Publishing Open Access dbid: RHX link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3Nb9MwFLdgAokLAgYsMCYjDS4owo1jOz4WtGm7TBMUqbco_lojtenUpiD-e95L3GgdoO0Y-dmO_LPfR_L8e4QcSzXCotwm1UZ7CFBUSDVzIlUhV1JUztgqdMUm1MVFMZ3qy0iStP77Fz5YOwzPs89c4DVJ0LUPC4GZW9_OpluFCw5Hn1oo4bwg2_s2v_1W3x3LE_Xv4xlGvr_qf_mXt9Mkb9id02fkaXQY6bhH-Dl54JsXZH_cQLC8-E0_0i6Fs_s2vk8alOopIej5Da7N9AvYKke_Y223zTVqh_5xHtJJLBFBx_Or5apuZwtaN_SyagetSCcdNvR8AaoHOl0t4nWl5iX5cXoy-XqWxoIKqQW73abeOa2dlUZYZrQZGaWsyXKlHeOZDlJZCJcC84aFwJU3DsIVoV3wFpwmzh1_RfaaZeMPCDU2V1YIqQsp84oxwNRVzGWeg0_HfUjIp-1ilzayjWPRi3nZRR1ClAhNGaFJyIdB-rpn2fiP3CHiVuLhg9EsrJotxwqmzBUvioQcRzzvGmULdhlP7LrMZAGuk2BcJOT90IwTYBZa45ebXgavFrNRQl73e2OYiCOxpFDQonZ2zSCAPN67LU096_i8dcYKLbM393v7t-QJPmK62ogfkr12tfHvyCP7s63Xq6PuZPwB5esHoA priority: 102 providerName: Hindawi Publishing |
| Title | Artificial Intelligence-Based Semisupervised Self-Training Algorithm in Pathological Tissue Image Segmentation |
| URI | https://dx.doi.org/10.1155/2022/3500592 https://www.ncbi.nlm.nih.gov/pubmed/35733571 https://www.proquest.com/docview/2680915035 https://www.proquest.com/docview/2680235801 https://pubmed.ncbi.nlm.nih.gov/PMC9208962 |
| Volume | 2022 |
| WOSCitedRecordID | wos000823736700019&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 1687-5273 dateEnd: 20250131 omitProxy: false ssIdentifier: ssj0057502 issn: 1687-5265 databaseCode: M7P dateStart: 20080101 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Computer Science Database customDbUrl: eissn: 1687-5273 dateEnd: 20250131 omitProxy: false ssIdentifier: ssj0057502 issn: 1687-5265 databaseCode: K7- dateStart: 20080101 isFulltext: true titleUrlDefault: http://search.proquest.com/compscijour providerName: ProQuest – providerCode: PRVPQU databaseName: Engineering Database customDbUrl: eissn: 1687-5273 dateEnd: 20250131 omitProxy: false ssIdentifier: ssj0057502 issn: 1687-5265 databaseCode: M7S dateStart: 20080101 isFulltext: true titleUrlDefault: http://search.proquest.com providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1687-5273 dateEnd: 20250131 omitProxy: false ssIdentifier: ssj0057502 issn: 1687-5265 databaseCode: 7X7 dateStart: 20080101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: Middle East & Africa Database customDbUrl: eissn: 1687-5273 dateEnd: 20250131 omitProxy: false ssIdentifier: ssj0057502 issn: 1687-5265 databaseCode: CWDGH dateStart: 20080101 isFulltext: true titleUrlDefault: https://search.proquest.com/middleeastafrica providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest advanced technologies & aerospace journals customDbUrl: eissn: 1687-5273 dateEnd: 20250131 omitProxy: false ssIdentifier: ssj0057502 issn: 1687-5265 databaseCode: P5Z dateStart: 20080101 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1687-5273 dateEnd: 20250131 omitProxy: false ssIdentifier: ssj0057502 issn: 1687-5265 databaseCode: BENPR dateStart: 20080101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Publicly Available Content Database customDbUrl: eissn: 1687-5273 dateEnd: 20250131 omitProxy: false ssIdentifier: ssj0057502 issn: 1687-5265 databaseCode: PIMPY dateStart: 20080101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVWIB databaseName: Wiley Online Library Open Access customDbUrl: eissn: 1687-5273 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0057502 issn: 1687-5265 databaseCode: 24P dateStart: 20070101 isFulltext: true titleUrlDefault: https://authorservices.wiley.com/open-science/open-access/browse-journals.html providerName: Wiley-Blackwell |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpR1db9Mw0GIbSLzwNQaBURlp8IKiuXFsx0-oQ5tWIapoK1LhJao_slZq07K2IP49d4lbVsTHAy-WrLvYce58H875jpAjqdpYlNvE2mgPDooqY82ciFWZKimGzthhWRebUL1eNhjoPBy4LUJY5Vom1oLazSyekR8nMgPVJhgXb-dfYqwahX9XQwmNHbLXTsDWB37Oxee1JAZLpIk5lLCRMA38OvBdCPT5k2Mu8O5lsqWSgmC-M0KX-Nv4d4bnr_GTNxTS2f3_XcoDci-YorTT8M5DcstXj8h-pwI3fPqdvqZ1cGh96r5PKsRqkk3Q7o0snvEJaEFHL7Fq3GqOcqfpTsq4H4pP0M7kCmZfjqZ0XNF8uNzIW9qvqU67UxBq8NDVNFyEqh6Tj2en_XfncSjVEFuwCJaxd05rZ6URlhlt2kYpa5JUacd4okupLDhiJfOGlSVX3jhwhIR2pbdgjnHu-AHZrWaVf0qosamyQkidSZkOGQNucUPmEs_BWuS-jMibNbUKG_KYYzmNSVH7M0IUSNsi0DYirzbY8yZ_xx_wDpHwBW5rGM3CV7NFR8GUqeJZFpGjwBD_GmVN8SLIgkXxk9wRebkB4wQY31b52arBwUvLrB2RJw1zbSbimLJSKICoLbbbIGCG8G1INR7VmcJ1wjItk2d_f63n5C4uAgPg2vyQ7C6vV_4FuW2_LseL6xbZUQNVt1mL7J2c9vIL6L1XcQvjZPO6vWzVGw_gefdD_gl6F-eDHz03MYE |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1ZbxMxEB6VAoIXrnIEChip5QWt6qzj9foBoXBUjVqiSgSp6suyPraJlGxCs6Hqn-I3MrNHaBDHUx94XHnWXnu_uezxDMBWpNpUlNsE2miPDorKAs2dDFTWUZFMnbFpVhabUP1-fHSkD9fge3MXhsIqG5lYCmo3tbRHvhNGMao2yYV8M_saUNUoOl1tSmhUsNj352foss1f997j_90Ow90Pg3d7QV1VILCovIrAO6e1s5GRlhtt2kYpa8KO0o6LUGeRsugzZNwbnmVCeePQZpfaZd6i5SCEE9jvFbiKD5oEwaE8biQ_Wj5VjGOEjEtp55tAeylpjyHcEZLueoYrKrBWBNeH5IKfjX5n6P4ar3lBAe7e_t-W7g7cqk1t1q144y6s-fwebHTztJhOztlLVga_lqcKG5ATVZVMg_UuZCkN3qKWd-wTVcVbzEiuVo_jLBjUxTVYd3yCsy2GEzbK2WFaLPUJG5SoZr0JCm186WRSX_TK78PnS5n5A1jPp7l_BMzYjrJSRjqOok7KOXKDS7kLvUBrWPisBa8adCS2ztNO5ULGSemvSZkQlpIaSy3YXlLPqvwkf6DbJKAlJLawN4urZpOuwiE7SsRxC7ZqAP6rlwZhSS3r5slPeLXgxbKZBqD4vdxPFxUNXcrm7RY8rMC8HEhQSk6psEWtwHxJQBnQV1vy0bDMhK5DHusofPz3z3oON_YGHw-Sg15__wncpAlRsF9bbMJ6cbrwT-Ga_VaM5qfPSnZm8OWymeAH1YOHng |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1bb9MwFLZGB4gXbuNSGGCkjRcUNbXjOH5AqDAqqkEViSJtTyHxZa3UpmVNmfbX-HWck0tZEZenPfAY-cSOne_c7ONzCNkLZReLcmeeypQFB0U6T_lGeNIFMhSpyXTqymITcjiMjo5UvEW-N3dhMKyykYmloDZzjXvkHRZGoNqEz0XH1WER8UH_9eKrhxWk8KS1KadRQeTQnp-B-7Z8NTiAf73PWP_d6O17r64w4GlQZIVnjVHK6DAT2s9U1s2k1BkLpDI-Z8qFUoP_4Hyb-c5xaTMD9rtQxlkNVgTnhkO_V8g2mOQsaJHtePAxPm70ANhBVcRjCGyMSeibsHshcMeBdbjAm59sQyHWauHaGB3ys8nvzN5fozcvqMP-rf95IW-Tm7URTnsV19whWza_S3Z6eVrMZ-f0BS3DYsvzhh2SI1WVZoMOLuQv9d6A_jf0E9bLWy1Q4laPU-eN6rIbtDc9gdkW4xmd5DROi7WmoaMS73QwA3EOL53M6itg-T3y-VJmfp-08nluHxKa6UBqIUIVhWGQ-j7wiUl9wywHO5lb1yYvG6Qkus7gjoVEpknpyQmRIK6SGldtsr-mXlSZS_5At4ugS1CgQW8aVk0nPQlDBpJHUZvs1WD8Vy8N2pJaCi6Tn1Brk-frZhwAI_tyO19VNHhd2--2yYMK2OuBOCbrFBJa5Abk1wSYG32zJZ-MyxzpivmRCtmjv3_WM3IdsJ98GAwPH5MbOB-MAuzyXdIqTlf2CbmqvxWT5enTmrcp-XLZXPAD6PGRxQ |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Artificial+Intelligence-Based+Semisupervised+Self-Training+Algorithm+in+Pathological+Tissue+Image+Segmentation&rft.jtitle=Computational+intelligence+and+neuroscience&rft.au=Li%2C+Qun&rft.au=Liu%2C+Linlin&rft.date=2022-06-13&rft.pub=John+Wiley+%26+Sons%2C+Inc&rft.issn=1687-5265&rft.eissn=1687-5273&rft.volume=2022&rft_id=info:doi/10.1155%2F2022%2F3500592&rft.externalDBID=HAS_PDF_LINK |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1687-5265&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1687-5265&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1687-5265&client=summon |