Semi-Supervised Variational Autoencoder for Cell Feature Extraction In Multiplexed Immunofluorescence Images
Advancements in digital imaging technologies have sparked increased interest in using multiplexed immunofluorescence (mIF) images to visualise and identify the interactions between specific immunophenotypes with the tumour microenvironment at the cellular level. Current state-of-the-art multiplexed...
Uložené v:
| Vydané v: | Proceedings (International Symposium on Biomedical Imaging) s. 1 - 5 |
|---|---|
| Hlavní autori: | , , , , , , , , |
| Médium: | Konferenčný príspevok.. |
| Jazyk: | English |
| Vydavateľské údaje: |
IEEE
27.05.2024
|
| Predmet: | |
| ISSN: | 1945-8452 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Advancements in digital imaging technologies have sparked increased interest in using multiplexed immunofluorescence (mIF) images to visualise and identify the interactions between specific immunophenotypes with the tumour microenvironment at the cellular level. Current state-of-the-art multiplexed immunofluorescence image analysis pipelines depend on cell feature representations characterised by morphological and stain intensity-based metrics generated using simple statistical and machine learning-based tools. However, these methods are not capable of generating complex representations of cells. We propose a deep learning-based cell feature extraction model using a variational autoencoder with supervision using a latent subspace to extract cell features in mIF images. We perform cell phenotype classification using a cohort of more than 44,000 multiplexed immunofluorescence cell image patches extracted across 1,093 tissue microarray cores of breast cancer patients, to demonstrate the success of our model against current and alternative methods. |
|---|---|
| AbstractList | Advancements in digital imaging technologies have sparked increased interest in using multiplexed immunofluorescence (mIF) images to visualise and identify the interactions between specific immunophenotypes with the tumour microenvironment at the cellular level. Current state-of-the-art multiplexed immunofluorescence image analysis pipelines depend on cell feature representations characterised by morphological and stain intensity-based metrics generated using simple statistical and machine learning-based tools. However, these methods are not capable of generating complex representations of cells. We propose a deep learning-based cell feature extraction model using a variational autoencoder with supervision using a latent subspace to extract cell features in mIF images. We perform cell phenotype classification using a cohort of more than 44,000 multiplexed immunofluorescence cell image patches extracted across 1,093 tissue microarray cores of breast cancer patients, to demonstrate the success of our model against current and alternative methods. |
| Author | Millar, Ewan K.A. Slapetova, Iveta Meijering, Erik Graham, Peter H. Song, Yang Sandarenu, Piumi Browne, Lois Swarbrick, Alexander Chen, Julia |
| Author_xml | – sequence: 1 givenname: Piumi surname: Sandarenu fullname: Sandarenu, Piumi organization: University of New South Wales,School of Computer Science and Engineering,Sydney,Australia – sequence: 2 givenname: Julia surname: Chen fullname: Chen, Julia organization: Garvan Institute of Medical Research,Cancer Ecosystems Program,Darlinghurst,Australia – sequence: 3 givenname: Iveta surname: Slapetova fullname: Slapetova, Iveta organization: St George Hospital,Cancer Care Centre,Kogarah,Australia – sequence: 4 givenname: Lois surname: Browne fullname: Browne, Lois organization: St George Hospital,Cancer Care Centre,Kogarah,Australia – sequence: 5 givenname: Peter H. surname: Graham fullname: Graham, Peter H. organization: St George Hospital,Cancer Care Centre,Kogarah,Australia – sequence: 6 givenname: Alexander surname: Swarbrick fullname: Swarbrick, Alexander organization: Garvan Institute of Medical Research,Cancer Ecosystems Program,Darlinghurst,Australia – sequence: 7 givenname: Ewan K.A. surname: Millar fullname: Millar, Ewan K.A. organization: UNSW Sydney,St. George & Sutherland Clinical School,Kensington,Australia – sequence: 8 givenname: Yang surname: Song fullname: Song, Yang organization: University of New South Wales,School of Computer Science and Engineering,Sydney,Australia – sequence: 9 givenname: Erik surname: Meijering fullname: Meijering, Erik organization: University of New South Wales,School of Computer Science and Engineering,Sydney,Australia |
| BookMark | eNo1kN9KwzAYxaMoqHNvIJgX6EyaP00u59i0MPFi6u342n6RSNqMtJX59lbUc3PgcPjBOVfkrIsdEnLL2YJzZu_K3X2ptCrYIme5XHCmheKsOCFzW1gjFBNcCGFOySW3UmVGqvyCzPv-g00qpBRMXpKww9Znu_GA6dP32NA3SB4GHzsIdDkOEbs6Npioi4muMAS6QRjGhHR9HBLUP01advRpDIM_BDxOiLJtxy66MMaEfT0BcIrgHftrcu4g9Dj_8xl53axfVo_Z9vmhXC23meeFHjI37XGgNDC02mpWFbVpjHGsahS3PG-0kVBJraytK6VACCV4LYxrpuEASszIzS_XI-L-kHwL6Wv_f5D4Bm3WXd8 |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.1109/ISBI56570.2024.10635107 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Xplore POP ALL 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 | 9798350313338 |
| EISSN | 1945-8452 |
| EndPage | 5 |
| ExternalDocumentID | 10635107 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: National Computational Infrastructure funderid: 10.13039/100010582 |
| 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-i176t-f202fa56a0e96960b7c8d88f0bd51912d684ab46599cb55a33531c38fd979aa53 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 0 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001305705100005&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 02:32:08 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | true |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i176t-f202fa56a0e96960b7c8d88f0bd51912d684ab46599cb55a33531c38fd979aa53 |
| PageCount | 5 |
| ParticipantIDs | ieee_primary_10635107 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-May-27 |
| PublicationDateYYYYMMDD | 2024-05-27 |
| PublicationDate_xml | – month: 05 year: 2024 text: 2024-May-27 day: 27 |
| PublicationDecade | 2020 |
| PublicationTitle | Proceedings (International Symposium on Biomedical Imaging) |
| PublicationTitleAbbrev | ISBI |
| PublicationYear | 2024 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| SSID | ssj0000744304 |
| Score | 2.2710357 |
| Snippet | Advancements in digital imaging technologies have sparked increased interest in using multiplexed immunofluorescence (mIF) images to visualise and identify the... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 1 |
| SubjectTerms | cell feature extraction Feature extraction Limiting Measurement Multiplexed immunofluorescence Multiplexing Phenotypes Pipelines semi-supervised variational autoencoder tumour microenvironment Visualization |
| Title | Semi-Supervised Variational Autoencoder for Cell Feature Extraction In Multiplexed Immunofluorescence Images |
| URI | https://ieeexplore.ieee.org/document/10635107 |
| WOSCitedRecordID | wos001305705100005&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/eLvHCXMwlV3NS8MwFA86POjFr4nf5OC1s23aJjnqmFjQMZjKbiNtXmDQtaNrZX--L1039eDBWygkPF54fZ-_Xwi5MwxMwCLtCJ36mKCgzUkPAsdIL4SQhdLwBij8wodDMZnIUQtWb7AwANAMn0HPLptevi7S2pbK0MLRPXoWO77LOV-DtbYFFfSFAebm7QyX58r7ePwY266ei2mgH_Q2u3-9o9K4kafDfwpwRLrfgDw62rqaY7ID-Qk5-MEleEqyMcxnzrheWONfgqYfmAW3lT76UFeFZazUUFKMUmkfsoza6K8ugQ5WVbmGN9A4p6_thOEKj4gteKQwWY1yWtYnlCKe4w9o2SXvT4O3_rPTPqXgzDweVY5BDRgVRsoFS4fjJjwVWgjjJhpDOM_XkQhUEkShlGkShooxtM2UCaMll0qF7Ix08iKHc0KND4aJxAVQPACtEqbB0ngJMErjgRekaxU3XazZMqYbnV3-8f2K7NvrsR15n1-TTlXWcEP20s9qtixvmzv-AonRqjI |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1bS8MwFA4yBfXF28S7efC12jZJmzzq2FhxDmEqvo20OYHBbnSt7Od70nVeHnzwLRQSDiecnuv3hZAby8ByFhlPmizEBAVtTgXAPasCAYIJZeMKKNyL-335_q6ea7B6hYUBgGr4DG7dsurlm1lWulIZWji6x8BhxzcF52Gwgmt9lVTQG3LMzusprsBXd8ngIXF9PR8TwZDfrvf_ekmlciSdvX-KsE-a35A8-vzlbA7IBkwPye4PNsEjMh7AZOQNyrkz_wUY-oZ5cF3ro_dlMXOclQZyinEqbcF4TF38V-ZA28siXwEcaDKlT_WM4RKPSBx8ZGbHJcrpeJ9QimSCv6BFk7x22i-trlc_puCNgjgqPIsasFpE2gdHiOOncSaNlNZPDQZxQWgiyXXKI6FUlgqhGUPrzJi0RsVKa8GOSWM6m8IJoTYEy2TqA-iYg9EpM-CIvCRYbfDAU9J0ihvOV3wZw7XOzv74fk22uy9PvWEv6T-ekx13Va4_H8YXpFHkJVySreyjGC3yq-q-PwGOaK15 |
| 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%3Abook&rft.genre=proceeding&rft.title=Proceedings+%28International+Symposium+on+Biomedical+Imaging%29&rft.atitle=Semi-Supervised+Variational+Autoencoder+for+Cell+Feature+Extraction+In+Multiplexed+Immunofluorescence+Images&rft.au=Sandarenu%2C+Piumi&rft.au=Chen%2C+Julia&rft.au=Slapetova%2C+Iveta&rft.au=Browne%2C+Lois&rft.date=2024-05-27&rft.pub=IEEE&rft.eissn=1945-8452&rft.spage=1&rft.epage=5&rft_id=info:doi/10.1109%2FISBI56570.2024.10635107&rft.externalDocID=10635107 |