{"id":"https://openalex.org/W2904204744","doi":"https://doi.org/10.1609/aaai.v33i01.33015901","title":"Biomedical Image Segmentation via Representative Annotation","display_name":"Biomedical Image Segmentation via Representative Annotation","publication_year":2019,"publication_date":"2019-07-17","ids":{"openalex":"https://openalex.org/W2904204744","doi":"https://doi.org/10.1609/aaai.v33i01.33015901","mag":"2904204744"},"language":"en","primary_location":{"id":"doi:10.1609/aaai.v33i01.33015901","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v33i01.33015901","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/4540/4418","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://ojs.aaai.org/index.php/AAAI/article/download/4540/4418","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100782616","display_name":"Hao Zheng","orcid":"https://orcid.org/0000-0001-7193-6242"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Hao Zheng","raw_affiliation_strings":["University of Notre Dame"],"affiliations":[{"raw_affiliation_string":"University of Notre Dame","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101725246","display_name":"Lin Yang","orcid":"https://orcid.org/0000-0002-1778-2059"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lin Yang","raw_affiliation_strings":["University of Notre Dame"],"affiliations":[{"raw_affiliation_string":"University of Notre Dame","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052870302","display_name":"Jianxu Chen","orcid":"https://orcid.org/0000-0002-8500-1357"},"institutions":[{"id":"https://openalex.org/I4210164942","display_name":"Allen Institute for Cell Science","ror":"https://ror.org/05kg6bp11","country_code":"US","type":"facility","lineage":["https://openalex.org/I4210140341","https://openalex.org/I4210164942"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jianxu Chen","raw_affiliation_strings":["Allen Institute for Cell Science"],"affiliations":[{"raw_affiliation_string":"Allen Institute for Cell Science","institution_ids":["https://openalex.org/I4210164942"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073124436","display_name":"Jun Han","orcid":"https://orcid.org/0000-0002-7286-062X"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jun Han","raw_affiliation_strings":["University of Notre Dame"],"affiliations":[{"raw_affiliation_string":"University of Notre Dame","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101959000","display_name":"Yizhe Zhang","orcid":"https://orcid.org/0000-0002-9599-7995"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yizhe Zhang","raw_affiliation_strings":["University of Notre Dame"],"affiliations":[{"raw_affiliation_string":"University of Notre Dame","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069345995","display_name":"Peixian Liang","orcid":"https://orcid.org/0000-0001-8600-3285"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Peixian Liang","raw_affiliation_strings":["University of Notre Dame"],"affiliations":[{"raw_affiliation_string":"University of Notre Dame","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059912382","display_name":"Zhuo Zhao","orcid":"https://orcid.org/0000-0002-4449-2663"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhuo Zhao","raw_affiliation_strings":["University of Notre Dame"],"affiliations":[{"raw_affiliation_string":"University of Notre Dame","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075201879","display_name":"Chaoli Wang","orcid":"https://orcid.org/0000-0002-6772-5191"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chaoli Wang","raw_affiliation_strings":["University of Notre Dame"],"affiliations":[{"raw_affiliation_string":"University of Notre Dame","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060901632","display_name":"Danny Z. Chen","orcid":"https://orcid.org/0000-0001-6565-2884"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Danny Z. Chen","raw_affiliation_strings":["University of Notre Dame"],"affiliations":[{"raw_affiliation_string":"University of Notre Dame","institution_ids":["https://openalex.org/I107639228"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":9,"corresponding_author_ids":["https://openalex.org/A5100782616"],"corresponding_institution_ids":["https://openalex.org/I107639228"],"apc_list":null,"apc_paid":null,"fwci":8.5782,"has_fulltext":true,"cited_by_count":64,"citation_normalized_percentile":{"value":0.98203593,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"33","issue":"01","first_page":"5901","last_page":"5908"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9987999796867371,"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"}},"topics":[{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9987999796867371,"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/T10862","display_name":"AI in cancer detection","score":0.996999979019165,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9932000041007996,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.8537697792053223},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7975452542304993},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7251380085945129},{"id":"https://openalex.org/keywords/automatic-image-annotation","display_name":"Automatic image annotation","score":0.6936959028244019},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6138484477996826},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5985429883003235},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5815553665161133},{"id":"https://openalex.org/keywords/image-retrieval","display_name":"Image retrieval","score":0.47924378514289856},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.464931458234787},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.45921894907951355},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.45029115676879883},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.42371276021003723},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.38832804560661316}],"concepts":[{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.8537697792053223},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7975452542304993},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7251380085945129},{"id":"https://openalex.org/C199579030","wikidata":"https://www.wikidata.org/wiki/Q2851778","display_name":"Automatic image annotation","level":4,"score":0.6936959028244019},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6138484477996826},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5985429883003235},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5815553665161133},{"id":"https://openalex.org/C1667742","wikidata":"https://www.wikidata.org/wiki/Q10927554","display_name":"Image retrieval","level":3,"score":0.47924378514289856},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.464931458234787},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45921894907951355},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.45029115676879883},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.42371276021003723},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.38832804560661316},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1609/aaai.v33i01.33015901","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v33i01.33015901","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/4540/4418","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:repository.hkust.edu.hk:1783.1-142351","is_oa":false,"landing_page_url":"http://repository.hkust.edu.hk/ir/Record/1783.1-142351","pdf_url":null,"source":{"id":"https://openalex.org/S4306401796","display_name":"Rare & Special e-Zone (The Hong Kong University of Science and Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I200769079","host_organization_name":"Hong Kong University of Science and Technology","host_organization_lineage":["https://openalex.org/I200769079"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Conference paper"}],"best_oa_location":{"id":"doi:10.1609/aaai.v33i01.33015901","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v33i01.33015901","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/4540/4418","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.6399999856948853,"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8"}],"awards":[{"id":"https://openalex.org/G1162675597","display_name":null,"funder_award_id":"-1629914","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3877151525","display_name":null,"funder_award_id":"IIS-1455886","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4360170584","display_name":"II-New: Infrastructure for Supporting Biomedical Application Algorithms, Runtime Development and Resource Management","funder_award_id":"1629914","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4687910176","display_name":"AF: Small: Algorithms in Computational Geometry and Medical Applications","funder_award_id":"1617735","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7420284228","display_name":null,"funder_award_id":"CNS-1629914","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7626749479","display_name":null,"funder_award_id":"CCF-1617735","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8226214622","display_name":"CAREER: Effective Analysis, Exploration and Visualization of Big Flow Data to Understand Dynamic Flows","funder_award_id":"1455886","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G848032724","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2904204744.pdf","grobid_xml":"https://content.openalex.org/works/W2904204744.grobid-xml"},"referenced_works_count":51,"referenced_works":["https://openalex.org/W168093571","https://openalex.org/W753012316","https://openalex.org/W1522301498","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1936642637","https://openalex.org/W1959608418","https://openalex.org/W2099471712","https://openalex.org/W2154642048","https://openalex.org/W2173520492","https://openalex.org/W2194775991","https://openalex.org/W2266059378","https://openalex.org/W2288892845","https://openalex.org/W2337429362","https://openalex.org/W2337735138","https://openalex.org/W2402144811","https://openalex.org/W2460470859","https://openalex.org/W2463818697","https://openalex.org/W2464708700","https://openalex.org/W2482581235","https://openalex.org/W2515788890","https://openalex.org/W2546066744","https://openalex.org/W2559597482","https://openalex.org/W2625559849","https://openalex.org/W2741891296","https://openalex.org/W2750925197","https://openalex.org/W2759532730","https://openalex.org/W2766736793","https://openalex.org/W2784962210","https://openalex.org/W2787445774","https://openalex.org/W2788014331","https://openalex.org/W2797248706","https://openalex.org/W2805605684","https://openalex.org/W2899771611","https://openalex.org/W2951123255","https://openalex.org/W2952178620","https://openalex.org/W2953384591","https://openalex.org/W2963122731","https://openalex.org/W2963446712","https://openalex.org/W2963684088","https://openalex.org/W2963803174","https://openalex.org/W2963977677","https://openalex.org/W2964121744","https://openalex.org/W2964317695","https://openalex.org/W4308909683","https://openalex.org/W4320013936","https://openalex.org/W6631190155","https://openalex.org/W6687483927","https://openalex.org/W6718740399","https://openalex.org/W6725739302","https://openalex.org/W6756040250"],"related_works":["https://openalex.org/W2566406229","https://openalex.org/W3177930984","https://openalex.org/W2052697133","https://openalex.org/W2119028572","https://openalex.org/W2152482390","https://openalex.org/W2376984068","https://openalex.org/W2076896210","https://openalex.org/W2365617273","https://openalex.org/W2506386910","https://openalex.org/W2117928543"],"abstract_inverted_index":{"Deep":[0],"learning":[1,29,63,78,177],"has":[2],"been":[3],"applied":[4],"successfully":[5],"to":[6,14,152,186],"many":[7],"biomedical":[8,20,40,85],"image":[9,21,44,86,98,131,134,157],"segmentation":[10,211],"tasks.":[11],"However,":[12],"due":[13],"the":[15,103,113,128,146,171],"diversity":[16],"and":[17,34,55,95,167,202,205],"complexity":[18],"of":[19,58,106,148,156,174],"data,":[22],"manual":[23,165],"annotation":[24,65,73,82,101,166,179],"for":[25,80,92,100,133,164],"training":[26],"common":[27,175],"deep":[28,77,149],"models":[30],"is":[31,124],"very":[32],"timeconsuming":[33],"labor-intensive,":[35],"especially":[36],"because":[37],"normally":[38],"only":[39],"experts":[41,48],"can":[42,183],"annotate":[43],"data":[45,115],"well.":[46],"Human":[47],"are":[49],"often":[50],"involved":[51],"in":[52,61,84,102],"a":[53,75],"long":[54],"iterative":[56,172],"process":[57,173],"annotation,":[59],"as":[60],"active":[62,176],"type":[64],"schemes.":[66],"In":[67],"this":[68],"paper,":[69],"we":[70],"propose":[71],"representative":[72,97],"(RA),":[74],"new":[76],"framework":[79,208],"reducing":[81],"effort":[83],"segmentation.":[87,135],"RA":[88,137,195],"uses":[89],"unsupervised":[90],"networks":[91,151],"feature":[93,108],"extraction":[94],"selects":[96],"patches":[99,132],"latent":[104],"space":[105],"learned":[107],"descriptors,":[109],"which":[110],"implicitly":[111],"characterizes":[112],"underlying":[114],"while":[116],"minimizing":[117],"redundancy.":[118],"A":[119],"fully":[120],"convolutional":[121],"network":[122],"(FCN)":[123],"then":[125],"trained":[126],"using":[127,197],"annotated":[129],"selected":[130],"Our":[136],"scheme":[138],"offers":[139],"three":[140,198],"compelling":[141],"advantages:":[142],"(1)":[143],"It":[144],"leverages":[145],"ability":[147],"neural":[150],"learn":[153],"better":[154],"representations":[155],"data;":[158],"(2)":[159],"it":[160,182],"performs":[161],"one-shot":[162],"selection":[163],"frees":[168],"annotators":[169],"from":[170],"based":[178],"schemes;":[180],"(3)":[181],"be":[184],"deployed":[185],"3D":[187],"images":[188],"with":[189,214],"simple":[190],"extensions.":[191],"We":[192],"evaluate":[193],"our":[194,207],"approach":[196],"datasets":[199],"(two":[200],"2D":[201],"one":[203],"3D)":[204],"show":[206],"yields":[209],"competitive":[210],"results":[212],"comparing":[213],"state-of-the-art":[215],"methods.":[216]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":11},{"year":2020,"cited_by_count":13},{"year":2019,"cited_by_count":4}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
