{"id":"https://openalex.org/W2936133355","doi":"https://doi.org/10.1109/isbi.2019.8759384","title":"Epithelial Segmentation From In Situ Hybridisation Histological Samples Using A Deep Central Attention Learning Approach","display_name":"Epithelial Segmentation From In Situ Hybridisation Histological Samples Using A Deep Central Attention Learning Approach","publication_year":2019,"publication_date":"2019-04-01","ids":{"openalex":"https://openalex.org/W2936133355","doi":"https://doi.org/10.1109/isbi.2019.8759384","mag":"2936133355"},"language":"en","primary_location":{"id":"doi:10.1109/isbi.2019.8759384","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi.2019.8759384","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://research.birmingham.ac.uk/en/publications/3721a2ff-db16-4081-b4fa-efa1c3b2ea2b","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5036968205","display_name":"Tzu\u2010Hsi Song","orcid":"https://orcid.org/0000-0002-3670-8970"},"institutions":[{"id":"https://openalex.org/I79619799","display_name":"University of Birmingham","ror":"https://ror.org/03angcq70","country_code":"GB","type":"education","lineage":["https://openalex.org/I79619799"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Tzu-Hsi Song","raw_affiliation_strings":["School of Dentistry, University of Birmingham, UK"],"affiliations":[{"raw_affiliation_string":"School of Dentistry, University of Birmingham, UK","institution_ids":["https://openalex.org/I79619799"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048758091","display_name":"Gabriel Landini","orcid":"https://orcid.org/0000-0002-9689-0989"},"institutions":[{"id":"https://openalex.org/I79619799","display_name":"University of Birmingham","ror":"https://ror.org/03angcq70","country_code":"GB","type":"education","lineage":["https://openalex.org/I79619799"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Gabriel Landini","raw_affiliation_strings":["School of Dentistry, University of Birmingham, UK"],"affiliations":[{"raw_affiliation_string":"School of Dentistry, University of Birmingham, UK","institution_ids":["https://openalex.org/I79619799"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080135413","display_name":"Shereen Fouad","orcid":"https://orcid.org/0000-0002-4965-7017"},"institutions":[{"id":"https://openalex.org/I12870472","display_name":"Birmingham City University","ror":"https://ror.org/00t67pt25","country_code":"GB","type":"education","lineage":["https://openalex.org/I12870472"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Shereen Fouad","raw_affiliation_strings":["Computing Engineering and Built Environment, Birmingham City University, UK"],"affiliations":[{"raw_affiliation_string":"Computing Engineering and Built Environment, Birmingham City University, UK","institution_ids":["https://openalex.org/I12870472"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008121589","display_name":"Hisham Mehanna","orcid":"https://orcid.org/0000-0002-5544-6224"},"institutions":[{"id":"https://openalex.org/I79619799","display_name":"University of Birmingham","ror":"https://ror.org/03angcq70","country_code":"GB","type":"education","lineage":["https://openalex.org/I79619799"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Hisham Mehanna","raw_affiliation_strings":["Institute of Cancer and Genomic Sciences, University of Birmingham, UK"],"affiliations":[{"raw_affiliation_string":"Institute of Cancer and Genomic Sciences, University of Birmingham, UK","institution_ids":["https://openalex.org/I79619799"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5036968205"],"corresponding_institution_ids":["https://openalex.org/I79619799"],"apc_list":null,"apc_paid":null,"fwci":0.4335,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.70792751,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1527","last_page":"1531"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.9879000186920166,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10146","display_name":"Cervical Cancer and HPV Research","score":0.98089998960495,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7222066521644592},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6684069633483887},{"id":"https://openalex.org/keywords/in-situ-hybridization","display_name":"In situ hybridization","score":0.6385665535926819},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5970454216003418},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5874571204185486},{"id":"https://openalex.org/keywords/deconvolution","display_name":"Deconvolution","score":0.5145537257194519},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5092033743858337},{"id":"https://openalex.org/keywords/in-situ","display_name":"In situ","score":0.5087648034095764},{"id":"https://openalex.org/keywords/immunohistochemistry","display_name":"Immunohistochemistry","score":0.49041271209716797},{"id":"https://openalex.org/keywords/pathology","display_name":"Pathology","score":0.42621850967407227},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.30886390805244446},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.0983600914478302},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.09386065602302551},{"id":"https://openalex.org/keywords/gene","display_name":"Gene","score":0.08114013075828552}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7222066521644592},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6684069633483887},{"id":"https://openalex.org/C81439078","wikidata":"https://www.wikidata.org/wiki/Q2304142","display_name":"In situ hybridization","level":4,"score":0.6385665535926819},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5970454216003418},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5874571204185486},{"id":"https://openalex.org/C174576160","wikidata":"https://www.wikidata.org/wiki/Q1183700","display_name":"Deconvolution","level":2,"score":0.5145537257194519},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5092033743858337},{"id":"https://openalex.org/C2777822432","wikidata":"https://www.wikidata.org/wiki/Q216681","display_name":"In situ","level":2,"score":0.5087648034095764},{"id":"https://openalex.org/C204232928","wikidata":"https://www.wikidata.org/wiki/Q899285","display_name":"Immunohistochemistry","level":2,"score":0.49041271209716797},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.42621850967407227},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.30886390805244446},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0983600914478302},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.09386065602302551},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.08114013075828552},{"id":"https://openalex.org/C150194340","wikidata":"https://www.wikidata.org/wiki/Q26972","display_name":"Gene expression","level":3,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/isbi.2019.8759384","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi.2019.8759384","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)","raw_type":"proceedings-article"},{"id":"pmh:oai:www.open-access.bcu.ac.uk:7311","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4306402654","display_name":"BCU Open Access Repository (Birmingham City University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I12870472","host_organization_name":"Birmingham City University","host_organization_lineage":["https://openalex.org/I12870472"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"","raw_type":"Conference or Workshop Item"},{"id":"pmh:oai:pure.atira.dk:openaire_cris_publications/3721a2ff-db16-4081-b4fa-efa1c3b2ea2b","is_oa":true,"landing_page_url":"https://research.birmingham.ac.uk/en/publications/3721a2ff-db16-4081-b4fa-efa1c3b2ea2b","pdf_url":null,"source":{"id":"https://openalex.org/S4306402634","display_name":"University of Birmingham Research Portal (University of Birmingham)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I79619799","host_organization_name":"University of Birmingham","host_organization_lineage":["https://openalex.org/I79619799"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Song, T-H, Landini, G, Fouad, S & Mehanna, H 2019, Epithelial segmentation from in situ hybridisation histological samples using a deep central attention learning approach. in 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)., 8759384, Proceedings - International Symposium on Biomedical Imaging, vol. 2019-April, Institute of Electrical and Electronics Engineers (IEEE), pp. 1527-1531, 2019 IEEE 16th International Symposium on Biomedical Imaging , Venice, Italy, 8/04/19. https://doi.org/10.1109/ISBI.2019.8759384","raw_type":"contributionToPeriodical"},{"id":"pmh:oai:pure.atira.dk:publications/3721a2ff-db16-4081-b4fa-efa1c3b2ea2b","is_oa":false,"landing_page_url":"https://ieeexplore.ieee.org/document/8759384","pdf_url":null,"source":{"id":"https://openalex.org/S4306402634","display_name":"University of Birmingham Research Portal (University of Birmingham)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I79619799","host_organization_name":"University of Birmingham","host_organization_lineage":["https://openalex.org/I79619799"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":""}],"best_oa_location":{"id":"pmh:oai:pure.atira.dk:openaire_cris_publications/3721a2ff-db16-4081-b4fa-efa1c3b2ea2b","is_oa":true,"landing_page_url":"https://research.birmingham.ac.uk/en/publications/3721a2ff-db16-4081-b4fa-efa1c3b2ea2b","pdf_url":null,"source":{"id":"https://openalex.org/S4306402634","display_name":"University of Birmingham Research Portal (University of Birmingham)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I79619799","host_organization_name":"University of Birmingham","host_organization_lineage":["https://openalex.org/I79619799"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Song, T-H, Landini, G, Fouad, S & Mehanna, H 2019, Epithelial segmentation from in situ hybridisation histological samples using a deep central attention learning approach. in 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)., 8759384, Proceedings - International Symposium on Biomedical Imaging, vol. 2019-April, Institute of Electrical and Electronics Engineers (IEEE), pp. 1527-1531, 2019 IEEE 16th International Symposium on Biomedical Imaging , Venice, Italy, 8/04/19. https://doi.org/10.1109/ISBI.2019.8759384","raw_type":"contributionToPeriodical"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2909887182","display_name":"Novel context-based segmentation algorithms for intelligent microscopy","funder_award_id":"EP/M023869/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G4112812085","display_name":null,"funder_award_id":"EP/M023869/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"}],"funders":[{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1597336200","https://openalex.org/W1901129140","https://openalex.org/W2118246710","https://openalex.org/W2167510172","https://openalex.org/W2194775991","https://openalex.org/W2254785591","https://openalex.org/W2282915343","https://openalex.org/W2302255633","https://openalex.org/W2315361044","https://openalex.org/W2528858179","https://openalex.org/W6635816984","https://openalex.org/W6639824700","https://openalex.org/W6684372118","https://openalex.org/W6698183232"],"related_works":["https://openalex.org/W2790662084","https://openalex.org/W2205128645","https://openalex.org/W2960184797","https://openalex.org/W2285304842","https://openalex.org/W4285827401","https://openalex.org/W3104734424","https://openalex.org/W4226289457","https://openalex.org/W3143622321","https://openalex.org/W2954384599","https://openalex.org/W2084088379"],"abstract_inverted_index":{"The":[0],"assessment":[1],"of":[2,40],"pathological":[3],"samples":[4,48],"by":[5,49],"molecular":[6],"techniques,":[7],"such":[8],"as":[9],"in":[10,31,45,74],"situ":[11],"hybridization":[12],"(ISH)":[13],"and":[14,54,58,91,98,144],"immunohistochemistry":[15],"(IHC),":[16],"has":[17],"revolutionised":[18],"modern":[19],"Histopathology.":[20],"Most":[21],"often":[22],"it":[23],"is":[24,52,109],"important":[25],"to":[26,70,86,111,116],"detect":[27],"ISH/IHC":[28],"reaction":[29],"products":[30,51],"certain":[32],"cells":[33],"or":[34],"tissue":[35,76],"types.":[36],"For":[37],"instance,":[38],"detection":[39],"human":[41],"papilloma":[42],"virus":[43],"(HPV)":[44],"oropharyngeal":[46,75],"cancer":[47],"ISH":[50,79],"difficult":[53],"remains":[55],"a":[56,67,88,103,139],"tedious":[57],"time":[59],"consuming":[60],"task":[61],"for":[62],"experts.":[63],"Here":[64],"we":[65,82],"introduce":[66],"proposed":[68,128],"framework":[69],"segment":[71],"epithelial":[72,114],"regions":[73,115],"images":[77],"with":[78,130],"staining.":[80],"First,":[81],"use":[83],"colour":[84],"deconvolution":[85],"obtain":[87],"counterstain":[89],"channel":[90],"generate":[92],"input":[93],"patches":[94],"based":[95],"on":[96],"superpixels":[97],"their":[99],"neighbouring":[100],"areas.":[101],"Then,":[102],"novel":[104],"deep":[105,133],"attention":[106],"residual":[107],"network":[108,129,137],"applied":[110],"identify":[112],"the":[113,123,127],"produce":[117],"an":[118],"epithelium":[119],"segmentation":[120],"mask.":[121],"In":[122],"experimental":[124],"results,":[125],"comparing":[126],"other":[131],"state-of-the-art":[132],"learning":[134],"approaches,":[135],"our":[136],"provides":[138],"better":[140],"performance":[141],"than":[142],"region-based":[143],"pixel-based":[145],"segmentations.":[146]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}