{"id":"https://openalex.org/W3027040251","doi":"https://doi.org/10.1109/isbi45749.2020.9098516","title":"Adversarial-Prediction Guided Multi-Task Adaptation for Semantic Segmentation of Electron Microscopy Images","display_name":"Adversarial-Prediction Guided Multi-Task Adaptation for Semantic Segmentation of Electron Microscopy Images","publication_year":2020,"publication_date":"2020-04-01","ids":{"openalex":"https://openalex.org/W3027040251","doi":"https://doi.org/10.1109/isbi45749.2020.9098516","mag":"3027040251"},"language":"en","primary_location":{"id":"doi:10.1109/isbi45749.2020.9098516","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi45749.2020.9098516","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)","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/A5071796767","display_name":"Jiajin Yi","orcid":"https://orcid.org/0000-0003-3849-6753"},"institutions":[{"id":"https://openalex.org/I119045251","display_name":"Huaqiao University","ror":"https://ror.org/03frdh605","country_code":"CN","type":"education","lineage":["https://openalex.org/I119045251"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jiajin Yi","raw_affiliation_strings":["College of Computer Science and Technology, Huaqiao University, Xiamen, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Huaqiao University, Xiamen, China","institution_ids":["https://openalex.org/I119045251"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016848462","display_name":"Zhimin Yuan","orcid":"https://orcid.org/0000-0003-4587-8370"},"institutions":[{"id":"https://openalex.org/I119045251","display_name":"Huaqiao University","ror":"https://ror.org/03frdh605","country_code":"CN","type":"education","lineage":["https://openalex.org/I119045251"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhimin Yuan","raw_affiliation_strings":["College of Computer Science and Technology, Huaqiao University, Xiamen, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Huaqiao University, Xiamen, China","institution_ids":["https://openalex.org/I119045251"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002811823","display_name":"Jialin Peng","orcid":"https://orcid.org/0000-0002-1797-0762"},"institutions":[{"id":"https://openalex.org/I119045251","display_name":"Huaqiao University","ror":"https://ror.org/03frdh605","country_code":"CN","type":"education","lineage":["https://openalex.org/I119045251"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jialin Peng","raw_affiliation_strings":["College of Computer Science and Technology, Huaqiao University, Xiamen, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Huaqiao University, Xiamen, China","institution_ids":["https://openalex.org/I119045251"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5071796767"],"corresponding_institution_ids":["https://openalex.org/I119045251"],"apc_list":null,"apc_paid":null,"fwci":0.9471,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.80798852,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"2020","issue":null,"first_page":"1205","last_page":"1208"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9965000152587891,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9965000152587891,"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/T10857","display_name":"Advanced Electron Microscopy Techniques and Applications","score":0.9907000064849854,"subfield":{"id":"https://openalex.org/subfields/1315","display_name":"Structural Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.9836999773979187,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.8067124485969543},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7771776914596558},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7667982578277588},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6822932958602905},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6793029308319092},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5520374178886414},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5429939031600952},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4614696502685547},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.45362111926078796},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.44966474175453186},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.44487234950065613},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.44234994053840637},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07223698496818542}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8067124485969543},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7771776914596558},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7667982578277588},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6822932958602905},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6793029308319092},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5520374178886414},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5429939031600952},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4614696502685547},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.45362111926078796},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.44966474175453186},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.44487234950065613},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.44234994053840637},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07223698496818542},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/isbi45749.2020.9098516","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi45749.2020.9098516","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)","raw_type":"proceedings-article"},{"id":"mag:3190413139","is_oa":false,"landing_page_url":"https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=202002281290037687","pdf_url":null,"source":{"id":"https://openalex.org/S4306512817","display_name":"IEEE Conference Proceedings","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":"IEEE Conference Proceedings","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.75,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1731081199","https://openalex.org/W1901129140","https://openalex.org/W2159291411","https://openalex.org/W2209448324","https://openalex.org/W2478454054","https://openalex.org/W2593768305","https://openalex.org/W2803684675","https://openalex.org/W2963107255","https://openalex.org/W2963820266","https://openalex.org/W2964121744","https://openalex.org/W2964288524","https://openalex.org/W2997305317","https://openalex.org/W4394560274","https://openalex.org/W6637618735","https://openalex.org/W6639824700","https://openalex.org/W6683633756","https://openalex.org/W6734871034","https://openalex.org/W6864221053"],"related_works":["https://openalex.org/W2965546495","https://openalex.org/W2361861616","https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3208297503","https://openalex.org/W3119773509","https://openalex.org/W2889153461","https://openalex.org/W2964117661","https://openalex.org/W4388405611","https://openalex.org/W2619127353"],"abstract_inverted_index":{"Semantic":[0],"segmentation":[1,93],"is":[2,26,78],"an":[3,54,85],"essential":[4],"step":[5],"for":[6,21,67,90,99],"electron":[7],"microscopy":[8],"(EM)":[9],"image":[10],"analysis.":[11],"Although":[12],"supervised":[13,34,92,113],"models":[14,36],"have":[15],"achieved":[16],"significant":[17],"progress,":[18],"the":[19,61,91,103,108,112,123],"need":[20],"labor":[22],"intensive":[23],"pixel-wise":[24],"annotation":[25],"a":[27,42,64,70],"major":[28],"limitation.":[29],"To":[30,106],"complicate":[31],"matters":[32],"further,":[33],"learning":[35,125,129],"may":[37],"not":[38,88],"generalize":[39],"well":[40],"on":[41,69,80,94,116,138],"novel":[43,71],"dataset":[44],"due":[45],"to":[46,59],"domain":[47,96],"shift.":[48],"In":[49],"this":[50],"study,":[51],"we":[52,83,120],"introduce":[53],"adversarial-prediction":[55],"guided":[56],"multitask":[57],"network":[58],"learn":[60,84],"adaptation":[62],"of":[63,102,111,146],"well-trained":[65],"model":[66,115],"use":[68],"unlabeled":[72,117],"target":[73,81,104,118],"domain.":[74],"Since":[75],"no":[76],"label":[77],"available":[79],"domain,":[82,119],"encoding":[86],"representation":[87,124],"only":[89],"source":[95],"but":[97],"also":[98],"unsupervised":[100],"reconstruction":[101],"data.":[105],"improve":[107],"discriminative":[109],"ability":[110],"learned":[114],"further":[121],"guide":[122],"by":[126],"multi-level":[127],"adversarial":[128],"in":[130],"semantic":[131],"prediction":[132],"space.":[133],"Comparisons":[134],"and":[135,144],"ablation":[136],"study":[137],"public":[139],"benchmark":[140],"demonstrated":[141],"state-of-the-art":[142],"performance":[143],"effectiveness":[145],"our":[147],"approach.":[148]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2020,"cited_by_count":3}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}