{"id":"https://openalex.org/W4387968195","doi":"https://doi.org/10.1145/3581783.3613779","title":"CoCa: A Connectivity-Aware Cascade Framework for Histology Gland Segmentation","display_name":"CoCa: A Connectivity-Aware Cascade Framework for Histology Gland Segmentation","publication_year":2023,"publication_date":"2023-10-26","ids":{"openalex":"https://openalex.org/W4387968195","doi":"https://doi.org/10.1145/3581783.3613779"},"language":"en","primary_location":{"id":"doi:10.1145/3581783.3613779","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3581783.3613779","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Multimedia","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5082697814","display_name":"Yu Bai","orcid":"https://orcid.org/0000-0001-9923-7427"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yu Bai","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100335246","display_name":"Bo Zhang","orcid":"https://orcid.org/0000-0002-1210-8735"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Zhang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030447151","display_name":"Zheng Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zheng Zhang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068917997","display_name":"Wu Liu","orcid":"https://orcid.org/0000-0003-1633-7575"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wu Liu","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009702543","display_name":"Jinwen Li","orcid":"https://orcid.org/0009-0000-0041-7613"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinwen Li","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006019956","display_name":"Xiangyang Gong","orcid":"https://orcid.org/0000-0002-0631-9747"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangyang Gong","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100329067","display_name":"Wendong Wang","orcid":"https://orcid.org/0000-0002-6418-8087"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wendong Wang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5082697814"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.3497,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.66270873,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1598","last_page":"1606"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9998999834060669,"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":0.9998999834060669,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9962999820709229,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T14510","display_name":"Medical Imaging and Analysis","score":0.9857000112533569,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6979037523269653},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5845910310745239},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.558392345905304},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4982907772064209},{"id":"https://openalex.org/keywords/coca","display_name":"Coca","score":0.42200767993927},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3413581848144531},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32072097063064575},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.08858805894851685}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6979037523269653},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5845910310745239},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.558392345905304},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4982907772064209},{"id":"https://openalex.org/C2781313679","wikidata":"https://www.wikidata.org/wiki/Q66793593","display_name":"Coca","level":2,"score":0.42200767993927},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3413581848144531},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32072097063064575},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.08858805894851685},{"id":"https://openalex.org/C118552586","wikidata":"https://www.wikidata.org/wiki/Q7867","display_name":"Psychiatry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3581783.3613779","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3581783.3613779","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.5899999737739563,"id":"https://metadata.un.org/sdg/3"}],"awards":[{"id":"https://openalex.org/G3944096777","display_name":null,"funder_award_id":"20220484063","funder_id":"https://openalex.org/F4320334978","funder_display_name":"Beijing Nova Program"},{"id":"https://openalex.org/G506554767","display_name":null,"funder_award_id":"62072047","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320334978","display_name":"Beijing Nova Program","ror":"https://ror.org/034k14f91"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W2322020277","https://openalex.org/W2412782625","https://openalex.org/W2482581235","https://openalex.org/W2526499595","https://openalex.org/W2753886374","https://openalex.org/W2963270775","https://openalex.org/W2963803174","https://openalex.org/W2963881378","https://openalex.org/W2964309882","https://openalex.org/W2978005227","https://openalex.org/W2979873137","https://openalex.org/W2980134361","https://openalex.org/W2983868555","https://openalex.org/W2999044405","https://openalex.org/W3035358681","https://openalex.org/W3035524453","https://openalex.org/W3049194333","https://openalex.org/W3086762873","https://openalex.org/W3087738047","https://openalex.org/W3102977943","https://openalex.org/W3122412340","https://openalex.org/W3125564923","https://openalex.org/W3157441214","https://openalex.org/W3161293646","https://openalex.org/W3174708193","https://openalex.org/W3207140595","https://openalex.org/W4200195652","https://openalex.org/W4212935637","https://openalex.org/W4248133011","https://openalex.org/W4282959113","https://openalex.org/W4287667294","https://openalex.org/W4304080168","https://openalex.org/W4304091533"],"related_works":["https://openalex.org/W2738862710","https://openalex.org/W2419950109","https://openalex.org/W1592835980","https://openalex.org/W4244618322","https://openalex.org/W2047241159","https://openalex.org/W2125328758","https://openalex.org/W2972037610","https://openalex.org/W2257903476","https://openalex.org/W4249972412","https://openalex.org/W2462811320"],"abstract_inverted_index":{"Gland":[0],"segmentation":[1,51],"is":[2],"crucial":[3],"for":[4,49],"computer-aided":[5],"diagnosis":[6],"of":[7,28,39,108,111,125,157,180],"adenocarcinoma.":[8],"However,":[9],"Topologically":[10],"Critical":[11],"Areas":[12],"(TCAs),":[13],"such":[14],"as":[15],"background":[16],"tissues":[17],"between":[18],"two":[19],"adjacent":[20],"glands,":[21,112],"can":[22,101],"easily":[23],"cause":[24],"under-":[25,97],"or":[26,98],"over-connection":[27],"gland":[29,50,54,73,127,146],"topological":[30,138],"structures":[31],"that":[32,70],"may":[33],"lead":[34],"to":[35,57,79,91,120,128,135,144,153],"the":[36,40,77,103,122,137,172,178],"opposite":[37],"diagnostic":[38],"malignancy":[41],"degree.":[42],"Therefore,":[43],"we":[44,113],"provide":[45],"a":[46,65,115],"novel":[47],"perspective":[48],"by":[52],"incorporating":[53],"connectivity":[55,74,82,130],"information":[56,75],"locate":[58,80,129],"critical":[59],"errors":[60,83],"within":[61,160],"TCAs.":[62],"We":[63,132,148],"propose":[64,133],"Connectivity-Aware":[66],"Cascade":[67],"framework":[68],"(CoCa)":[69],"explicitly":[71],"encodes":[72],"into":[76],"network":[78],"all":[81],"during":[84],"training":[85],"and":[86,174],"then":[87],"leverage":[88],"attention":[89],"operations":[90],"focus":[92],"on":[93,171],"these":[94],"errors.":[95,131],"Since":[96],"over-connected":[99],"glands":[100],"change":[102],"Betti":[104,123],"number":[105,107,124],"(e.g.,":[106],"connected":[109],"components)":[110],"design":[114],"Connectivity":[116],"Refinement":[117],"Module":[118],"(CRM)":[119],"compare":[121],"each":[126],"CoCa-Net":[134],"mine":[136],"relations":[139],"among":[140],"different":[141,158],"biomedical":[142],"entities":[143],"guide":[145],"prediction.":[147],"also":[149],"use":[150],"contrastive":[151],"learning":[152],"separate":[154],"pixel":[155],"embeddings":[156],"classes":[159],"TCAs":[161],"through":[162],"our":[163],"connectivity-aware":[164],"hard":[165],"example":[166],"sampling":[167],"strategy.":[168],"Extensive":[169],"experiments":[170],"GlaS":[173],"CRAG":[175],"datasets":[176],"demonstrate":[177],"effectiveness":[179],"CoCa":[181],"over":[182],"state-of-the-art":[183],"methods.":[184]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}