Colorectal Cancer Tissue Classification Using Semi-Supervised Hypergraph Convolutional Network
Colorectal Cancer (CRC) is a leading cause of death around the globe, and therefore, the analysis of tumor micro environment in the CRC WSIs is important for the early detection of CRC. Conventional visual inspection is very time consuming and the process can undergo inaccuracies because of the subj...
Uloženo v:
| Vydáno v: | Proceedings (International Symposium on Biomedical Imaging) s. 1306 - 1309 |
|---|---|
| Hlavní autoři: | , , , , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
| Vydáno: |
IEEE
13.04.2021
|
| Témata: | |
| ISSN: | 1945-8452 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Colorectal Cancer (CRC) is a leading cause of death around the globe, and therefore, the analysis of tumor micro environment in the CRC WSIs is important for the early detection of CRC. Conventional visual inspection is very time consuming and the process can undergo inaccuracies because of the subject-level assessment. Deep learning has shown promising results in medical image analysis. However, these approaches require a lot of labeling images from medical experts. In this paper, we propose a semi-supervised algorithm for CRC tissue classification. We propose to employ the hypergraph neural network to classify seven different biologically meaningful CRC tissue types. Firstly, image deep features are extracted from input patches using the pre-trained VGG19 model. The hypergraph is then constructed whereby patch-level deep features represent the vertices of hypergraph and hyperedges are assigned using pair-wise euclidean distance. The edges, vertices, and their corresponding patch-level features are passed through a feed-forward neural network to perform tissue classification in a transductive manner. Experiments are performed on an independent CRC tissue classification dataset and compared with existing state-of-the-art methods. Our results reveal that the proposed algorithm outperforms existing methods by achieving an overall accuracy of 95.46% and AvTP of 94.42%. |
|---|---|
| AbstractList | Colorectal Cancer (CRC) is a leading cause of death around the globe, and therefore, the analysis of tumor micro environment in the CRC WSIs is important for the early detection of CRC. Conventional visual inspection is very time consuming and the process can undergo inaccuracies because of the subject-level assessment. Deep learning has shown promising results in medical image analysis. However, these approaches require a lot of labeling images from medical experts. In this paper, we propose a semi-supervised algorithm for CRC tissue classification. We propose to employ the hypergraph neural network to classify seven different biologically meaningful CRC tissue types. Firstly, image deep features are extracted from input patches using the pre-trained VGG19 model. The hypergraph is then constructed whereby patch-level deep features represent the vertices of hypergraph and hyperedges are assigned using pair-wise euclidean distance. The edges, vertices, and their corresponding patch-level features are passed through a feed-forward neural network to perform tissue classification in a transductive manner. Experiments are performed on an independent CRC tissue classification dataset and compared with existing state-of-the-art methods. Our results reveal that the proposed algorithm outperforms existing methods by achieving an overall accuracy of 95.46% and AvTP of 94.42%. |
| Author | Javed, Sajid Bakht, Ahsan Baidar AlMarzouqi, Hasan Khandoker, Ahsan Werghi, Naoufel |
| Author_xml | – sequence: 1 givenname: Ahsan Baidar surname: Bakht fullname: Bakht, Ahsan Baidar organization: Khalifa University,UAE – sequence: 2 givenname: Sajid surname: Javed fullname: Javed, Sajid organization: Khalifa University,UAE – sequence: 3 givenname: Hasan surname: AlMarzouqi fullname: AlMarzouqi, Hasan organization: Khalifa University,UAE – sequence: 4 givenname: Ahsan surname: Khandoker fullname: Khandoker, Ahsan organization: Khalifa University,UAE – sequence: 5 givenname: Naoufel surname: Werghi fullname: Werghi, Naoufel organization: Khalifa University,UAE |
| BookMark | eNotUFFLwzAYjKLgNvcLBMkf6MyXfG3TRy3qBkMfOl8dsfk6o10zkm6yf2_FHQd3cMc93JhddL4jxm5BzABEcbeoHhaoJcBMCgmzAhUKlZ2xMWRZiiAxg3M2ggLTRGMqr9g0xi8xIEdUAkfsvfStD1T3puWl6WoKfOVi3BMvWxOja1xteuc7_hZdt-EVbV1S7XcUDi6S5fPjYDfB7D556buDb_d_5WHrhfofH76v2WVj2kjTk07Y6ulxVc6T5evzorxfJk4K1ScKUkkm13mOmtBIa5SlgTQYzAktSCsVfGjTpMJik1lZD5ktchKp1mrCbv5nHRGtd8FtTTiuT2-oXzTDWJM |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.1109/ISBI48211.2021.9434036 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume IEEE Xplore All Conference Proceedings IEEE Electronic Library (IEL) IEEE Proceedings Order Plans (POP All) 1998-Present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISBN | 1665412461 9781665412469 |
| EISSN | 1945-8452 |
| EndPage | 1309 |
| ExternalDocumentID | 9434036 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: Terry Fox Foundation funderid: 10.13039/501100002655 |
| GroupedDBID | 23N 6IE 6IF 6IK 6IL 6IN AAJGR AAWTH ABLEC ADZIZ ALMA_UNASSIGNED_HOLDINGS BEFXN BFFAM BGNUA BKEBE BPEOZ CBEJK CHZPO IEGSK IPLJI M43 OCL RIE RIL RNS |
| ID | FETCH-LOGICAL-i203t-3152ea787748e4a2da3de3deeda347e4d12d231b8af50d4f6d2cedad97e05883 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 14 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000786144100272&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| IngestDate | Wed Aug 27 02:27:47 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | true |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i203t-3152ea787748e4a2da3de3deeda347e4d12d231b8af50d4f6d2cedad97e05883 |
| PageCount | 4 |
| ParticipantIDs | ieee_primary_9434036 |
| PublicationCentury | 2000 |
| PublicationDate | 2021-April-13 |
| PublicationDateYYYYMMDD | 2021-04-13 |
| PublicationDate_xml | – month: 04 year: 2021 text: 2021-April-13 day: 13 |
| PublicationDecade | 2020 |
| PublicationTitle | Proceedings (International Symposium on Biomedical Imaging) |
| PublicationTitleAbbrev | ISBI |
| PublicationYear | 2021 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| SSID | ssj0000744304 |
| Score | 2.233275 |
| Snippet | Colorectal Cancer (CRC) is a leading cause of death around the globe, and therefore, the analysis of tumor micro environment in the CRC WSIs is important for... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 1306 |
| SubjectTerms | Classification algorithms Colorectal Cancer (CRC) Deep Learning Feature extraction Hypergraph Neural Network Image edge detection Inspection Labeling Neural networks Tissue Classification Visualization |
| Title | Colorectal Cancer Tissue Classification Using Semi-Supervised Hypergraph Convolutional Network |
| URI | https://ieeexplore.ieee.org/document/9434036 |
| WOSCitedRecordID | wos000786144100272&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 | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LawIxEB5UemgvfWjpmxx67OpmN5tsrpWKXkTQg6dKdjMLQquyrv7-TuJiW-ilkENISAIzyUy-ZB4Az1mWa1VwEWgrMwIomgfGFiYo4phLrZJcaeuTTajxOJ3P9aQBL0dfGET0xmfYdVX_l2_X-c49lfVcLDOSuE1oKiUPvlrH9xRShYKgee0EzEPdG01fRyIlgEMoMOLdevCvLCpeiQzO_7f8BXS-vfHY5KhnLqGBqys4-xFIsA3vhP_XTnaZD9Z3fCzZzFOU-aSXzhzIc4B5CwE2xc9lMN1tnJzYomVDAqOlD13NaM19vRtprvHBSLwDs8HbrD8M6swJwTIK44oEaxKhobOoRIrCRNbEFqkgVYRCYXlk6WKXpaZIQisKaaOc-qxWGCZpGl9Da7Ve4Q2wzGCRI0eeZijodBu6z6CycSGkJDQkb6HtCLXYHGJjLGoa3f3dfA-njhfuN4bHD9Cqyh0-wkm-r5bb8skz9AsUnqRh |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV07T8MwED6VggQsPArijQdG0saJE8crFVUrSlSpHTpROfFF6kBbpY_fz9mNCkgsSBmsRH7oLr67z74HwFOW5UoWXHjKxBkBFMU9bQrtFWHIYyWjXCrjik3INE3GYzWowfMuFgYRnfMZNm3T3eWbeb62R2Utm8uMJO4e7EdCBP42Wmt3okLKUBA4r8KAua9aveFLTyQEcQgHBrxZdf9VR8Wpkc7J_xZwChff8XhssNM0Z1DD2Tkc_0gl2ICP9tyOlpMtzdqWkyUbOZoyV_bSOgQ5HjDnI8CG-Dn1huuFlRRLNKxLcLR0yasZzbmp_kcaK926iV_AqPM6ane9qnaCNw38cEWiNQpQ026UIkGhA6NDg_QgNYREYXhgyLTLEl1EvhFFbIKcvhkl0Y-SJLyE-mw-wytgmcYiR448yVDQ_tZk0aA0YSHimPBQfA0NS6jJYpsdY1LR6Obv149w2B299yf9Xvp2C0eWL_Zuhod3UF-Va7yHg3yzmi7LB8fcL81Hp6g |
| 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=proceeding&rft.title=Proceedings+%28International+Symposium+on+Biomedical+Imaging%29&rft.atitle=Colorectal+Cancer+Tissue+Classification+Using+Semi-Supervised+Hypergraph+Convolutional+Network&rft.au=Bakht%2C+Ahsan+Baidar&rft.au=Javed%2C+Sajid&rft.au=AlMarzouqi%2C+Hasan&rft.au=Khandoker%2C+Ahsan&rft.date=2021-04-13&rft.pub=IEEE&rft.eissn=1945-8452&rft.spage=1306&rft.epage=1309&rft_id=info:doi/10.1109%2FISBI48211.2021.9434036&rft.externalDocID=9434036 |