{"id":"https://openalex.org/W4392931672","doi":"https://doi.org/10.1109/tbdata.2024.3378062","title":"Weakly-Supervised Cross-Domain Segmentation of Electron Microscopy With Sparse Point Annotation","display_name":"Weakly-Supervised Cross-Domain Segmentation of Electron Microscopy With Sparse Point Annotation","publication_year":2024,"publication_date":"2024-03-18","ids":{"openalex":"https://openalex.org/W4392931672","doi":"https://doi.org/10.1109/tbdata.2024.3378062"},"language":"en","primary_location":{"id":"doi:10.1109/tbdata.2024.3378062","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tbdata.2024.3378062","pdf_url":null,"source":{"id":"https://openalex.org/S2491400915","display_name":"IEEE Transactions on Big Data","issn_l":"2332-7790","issn":["2332-7790","2372-2096"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Big Data","raw_type":"journal-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/A5066992016","display_name":"Dafei Qiu","orcid":null},"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":"Dafei Qiu","raw_affiliation_strings":["College of Computer Science and Technology, Huaqiao University, Xiamen, Fujian, China","College of Computer Science and Technology, Huaqiao University, Xiamen, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Huaqiao University, Xiamen, Fujian, China","institution_ids":["https://openalex.org/I119045251"]},{"raw_affiliation_string":"College of Computer Science and Technology, Huaqiao University, Xiamen, China","institution_ids":["https://openalex.org/I119045251"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Shan Xiong","orcid":"https://orcid.org/0009-0008-9579-606X"},"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":"Shan Xiong","raw_affiliation_strings":["College of Computer Science and Technology, Huaqiao University, Xiamen, Fujian, China","College of Computer Science and Technology, Huaqiao University, Xiamen, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Huaqiao University, Xiamen, Fujian, China","institution_ids":["https://openalex.org/I119045251"]},{"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/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":false,"raw_author_name":"Jiajin Yi","raw_affiliation_strings":["College of Computer Science and Technology, Huaqiao University, Xiamen, Fujian, China","College of Computer Science and Technology, Huaqiao University, Xiamen, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Huaqiao University, Xiamen, Fujian, China","institution_ids":["https://openalex.org/I119045251"]},{"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, Fujian, China","College of Computer Science and Technology, Huaqiao University, Xiamen, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Huaqiao University, Xiamen, Fujian, China","institution_ids":["https://openalex.org/I119045251"]},{"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":4,"corresponding_author_ids":["https://openalex.org/A5066992016"],"corresponding_institution_ids":["https://openalex.org/I119045251"],"apc_list":null,"apc_paid":null,"fwci":1.3627,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.82791703,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"11","issue":"2","first_page":"359","last_page":"371"},"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":0.9980000257492065,"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":0.9980000257492065,"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/T11948","display_name":"Machine Learning in Materials Science","score":0.9908000230789185,"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"}},{"id":"https://openalex.org/T10857","display_name":"Advanced Electron Microscopy Techniques and Applications","score":0.980400025844574,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.828026533126831},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.7217376232147217},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6906774044036865},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6264723539352417},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.553444504737854},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46795713901519775},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4564128518104553},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.4389907121658325},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.421843945980072},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.16463562846183777}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.828026533126831},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.7217376232147217},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6906774044036865},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6264723539352417},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.553444504737854},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46795713901519775},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4564128518104553},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.4389907121658325},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.421843945980072},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.16463562846183777}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tbdata.2024.3378062","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tbdata.2024.3378062","pdf_url":null,"source":{"id":"https://openalex.org/S2491400915","display_name":"IEEE Transactions on Big Data","issn_l":"2332-7790","issn":["2332-7790","2372-2096"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Big Data","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2099550197","display_name":null,"funder_award_id":"62276105","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7766801970","display_name":null,"funder_award_id":"11771160","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"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":56,"referenced_works":["https://openalex.org/W611457968","https://openalex.org/W1513082520","https://openalex.org/W1901129140","https://openalex.org/W2100213115","https://openalex.org/W2131415852","https://openalex.org/W2134820502","https://openalex.org/W2194775991","https://openalex.org/W2344654247","https://openalex.org/W2395611524","https://openalex.org/W2412782625","https://openalex.org/W2463631526","https://openalex.org/W2478454054","https://openalex.org/W2592905743","https://openalex.org/W2798643036","https://openalex.org/W2899476650","https://openalex.org/W2962758679","https://openalex.org/W2963107255","https://openalex.org/W2963820266","https://openalex.org/W2964264515","https://openalex.org/W2992308087","https://openalex.org/W2997305317","https://openalex.org/W2998115938","https://openalex.org/W2999219213","https://openalex.org/W3009610145","https://openalex.org/W3011974116","https://openalex.org/W3014178491","https://openalex.org/W3039259519","https://openalex.org/W3087075955","https://openalex.org/W3089720110","https://openalex.org/W3091734171","https://openalex.org/W3109470472","https://openalex.org/W3117221381","https://openalex.org/W3120127493","https://openalex.org/W3135022261","https://openalex.org/W3175308890","https://openalex.org/W3176969075","https://openalex.org/W3185801199","https://openalex.org/W3192337685","https://openalex.org/W3197282828","https://openalex.org/W3199551447","https://openalex.org/W4281759217","https://openalex.org/W4283011659","https://openalex.org/W4285010222","https://openalex.org/W4285081805","https://openalex.org/W4295935505","https://openalex.org/W4312527924","https://openalex.org/W4313413305","https://openalex.org/W4387675569","https://openalex.org/W6683633756","https://openalex.org/W6746282794","https://openalex.org/W6755410731","https://openalex.org/W6779403271","https://openalex.org/W6779632826","https://openalex.org/W6800305947","https://openalex.org/W6841838287","https://openalex.org/W6864221053"],"related_works":["https://openalex.org/W2361861616","https://openalex.org/W2263699433","https://openalex.org/W2377979023","https://openalex.org/W2218034408","https://openalex.org/W2392921965","https://openalex.org/W2358755282","https://openalex.org/W2625833328","https://openalex.org/W4283211831","https://openalex.org/W2798287483","https://openalex.org/W1522196789"],"abstract_inverted_index":{"Accurate":[0],"segmentation":[1,57,96],"of":[2,85,140,209,225,236,249],"organelle":[3],"instances":[4,87],"from":[5,23,62],"electron":[6],"microscopy":[7],"(EM)":[8],"images":[9],"plays":[10],"an":[11,182],"essential":[12],"role":[13],"in":[14,88,229],"many":[15],"neuroscience":[16],"researches.":[17],"However,":[18],"practical":[19,63],"scenarios":[20],"usually":[21],"suffer":[22],"high":[24,223],"annotation":[25,41,171,242],"costs,":[26],"label":[27,179],"scarcity,":[28],"and":[29,105,111,124,181,214,232],"large":[30],"domain":[31,35],"diversity.":[32],"While":[33],"unsupervised":[34],"adaptation":[36],"(UDA)":[37],"that":[38],"assumes":[39,77],"no":[40,133],"effort":[42],"on":[43,55,81],"the":[44,89,121,137,141,164,204,219,230,233,245],"target":[45,90],"data":[46],"is":[47,59,207],"promising":[48],"to":[49,117],"alleviate":[50],"these":[51,67],"challenges,":[52],"its":[53],"performance":[54,217],"complicated":[56],"tasks":[58,110,129],"still":[60],"far":[61],"usage.":[64],"To":[65,93,167],"address":[66],"issues,":[68],"we":[69,101,144,173],"investigate":[70],"a":[71,82,113,149,155,175],"highly":[72],"annotation-efficient":[73],"weak":[74,195],"supervision,":[75],"which":[76,126,161],"only":[78],"sparse":[79,199,240],"center-points":[80],"small":[83],"subset":[84],"object":[86],"training":[91],"images.":[92],"achieve":[94],"accurate":[95],"with":[97,130,148],"partial":[98,131],"point":[99,241],"annotations,":[100],"introduce":[102],"instance":[103],"counting":[104,146],"center":[106,159],"detection":[107],"as":[108,154,218],"auxiliary":[109],"design":[112],"multitask":[114],"learning":[115],"framework":[116],"leverage":[118],"correlations":[119],"among":[120],"counting,":[122],"detection,":[123,160],"segmentation,":[125],"are":[127],"all":[128],"or":[132],"supervision.":[134],"Building":[135],"upon":[136],"different":[138],"domain-invariances":[139],"three":[142],"tasks,":[143],"enforce":[145],"estimation":[147],"novel":[150],"soft":[151],"consistency":[152],"loss":[153],"global":[156],"prior":[157],"for":[158,170,178,201,239],"further":[162,168,243],"guides":[163],"per-pixel":[165],"segmentation.":[166],"compensate":[169],"sparsity,":[172],"develop":[174],"cross-position":[176],"cut-and-paste":[177],"augmentation":[180],"entropy-based":[183],"pseudo-label":[184],"selection.":[185],"The":[186,222],"experimental":[187],"results":[188],"highlight":[189],"that,":[190],"by":[191],"simply":[192],"using":[193],"extremely":[194],"annotation,":[196],"e.g.,":[197],"15%":[198],"points,":[200],"model":[202,206,227],"training,":[203],"proposed":[205],"capable":[208],"significantly":[210],"outperforming":[211],"UDA":[212],"methods":[213],"produces":[215],"comparable":[216],"supervised":[220],"counterpart.":[221],"robustness":[224],"our":[226,250],"shown":[228],"validations":[231],"low":[234],"requirement":[235],"expert":[237],"knowledge":[238],"improve":[244],"potential":[246],"application":[247],"value":[248],"model.":[251]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}
