{"id":"https://openalex.org/W3014178491","doi":"https://doi.org/10.1109/jstsp.2020.3005317","title":"Unsupervised Mitochondria Segmentation in EM Images via Domain Adaptive Multi-Task Learning","display_name":"Unsupervised Mitochondria Segmentation in EM Images via Domain Adaptive Multi-Task Learning","publication_year":2020,"publication_date":"2020-06-27","ids":{"openalex":"https://openalex.org/W3014178491","doi":"https://doi.org/10.1109/jstsp.2020.3005317","mag":"3014178491"},"language":"en","primary_location":{"id":"doi:10.1109/jstsp.2020.3005317","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jstsp.2020.3005317","pdf_url":null,"source":{"id":"https://openalex.org/S42167783","display_name":"IEEE Journal of Selected Topics in Signal Processing","issn_l":"1932-4553","issn":["1932-4553","1941-0484"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["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 Journal of Selected Topics in Signal Processing","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2004.02134","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","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":true,"raw_author_name":"Jialin Peng","raw_affiliation_strings":["Xiamen Key Laboratory of Computer Vision and Pattern Recognition, and the Fujian Key Lab of Big Data Intelligence and Security, College of Computer Science and Technology, Huaqiao University, Xiamen, China"],"raw_orcid":"https://orcid.org/0000-0002-1797-0762","affiliations":[{"raw_affiliation_string":"Xiamen Key Laboratory of Computer Vision and Pattern Recognition, and the Fujian Key Lab of Big Data Intelligence and Security, 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":["Xiamen Key Laboratory of Computer Vision and Pattern Recognition, and the Fujian Key Lab of Big Data Intelligence and Security, College of Computer Science and Technology, Huaqiao University, Xiamen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xiamen Key Laboratory of Computer Vision and Pattern Recognition, and the Fujian Key Lab of Big Data Intelligence and Security, College of Computer Science and Technology, Huaqiao University, Xiamen, China","institution_ids":["https://openalex.org/I119045251"]}]},{"author_position":"last","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":["Xiamen Key Laboratory of Computer Vision and Pattern Recognition, and the Fujian Key Lab of Big Data Intelligence and Security, College of Computer Science and Technology, Huaqiao University, Xiamen, China"],"raw_orcid":"https://orcid.org/0000-0003-4587-8370","affiliations":[{"raw_affiliation_string":"Xiamen Key Laboratory of Computer Vision and Pattern Recognition, and the Fujian Key Lab of Big Data Intelligence and Security, 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/A5002811823"],"corresponding_institution_ids":["https://openalex.org/I119045251"],"apc_list":null,"apc_paid":null,"fwci":2.582,"has_fulltext":true,"cited_by_count":29,"citation_normalized_percentile":{"value":0.91581289,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"14","issue":"6","first_page":"1199","last_page":"1209"},"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/T12169","display_name":"Non-Destructive Testing Techniques","score":0.9934999942779541,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"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":0.9776999950408936,"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":0.7849327325820923},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7685398459434509},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7615764141082764},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6857817769050598},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6506675481796265},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5611314177513123},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.533002495765686},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4990830421447754},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.4578167498111725},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4384705424308777},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.43350714445114136},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.43202126026153564},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.41723254323005676},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08333015441894531}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7849327325820923},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7685398459434509},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7615764141082764},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6857817769050598},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6506675481796265},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5611314177513123},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.533002495765686},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4990830421447754},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.4578167498111725},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4384705424308777},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.43350714445114136},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.43202126026153564},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.41723254323005676},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08333015441894531},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"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/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/jstsp.2020.3005317","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jstsp.2020.3005317","pdf_url":null,"source":{"id":"https://openalex.org/S42167783","display_name":"IEEE Journal of Selected Topics in Signal Processing","issn_l":"1932-4553","issn":["1932-4553","1941-0484"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["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 Journal of Selected Topics in Signal Processing","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2004.02134","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2004.02134","pdf_url":"https://arxiv.org/pdf/2004.02134","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2004.02134","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2004.02134","pdf_url":"https://arxiv.org/pdf/2004.02134","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.7300000190734863}],"awards":[{"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":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3014178491.pdf","grobid_xml":"https://content.openalex.org/works/W3014178491.grobid-xml"},"referenced_works_count":58,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1565327149","https://openalex.org/W1686810756","https://openalex.org/W1731081199","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W2025768430","https://openalex.org/W2053802831","https://openalex.org/W2159291411","https://openalex.org/W2194775991","https://openalex.org/W2209448324","https://openalex.org/W2312004824","https://openalex.org/W2395611524","https://openalex.org/W2478454054","https://openalex.org/W2562192638","https://openalex.org/W2584009249","https://openalex.org/W2593768305","https://openalex.org/W2803684675","https://openalex.org/W2962808524","https://openalex.org/W2962837118","https://openalex.org/W2963107255","https://openalex.org/W2963820266","https://openalex.org/W2963858786","https://openalex.org/W2964090697","https://openalex.org/W2964121744","https://openalex.org/W2964288524","https://openalex.org/W2964309882","https://openalex.org/W2970283086","https://openalex.org/W2972339784","https://openalex.org/W2981429991","https://openalex.org/W2997305317","https://openalex.org/W3027040251","https://openalex.org/W4394560274","https://openalex.org/W6631190155","https://openalex.org/W6633949838","https://openalex.org/W6637373629","https://openalex.org/W6637618735","https://openalex.org/W6639824700","https://openalex.org/W6640054144","https://openalex.org/W6657028776","https://openalex.org/W6683633756","https://openalex.org/W6687483927","https://openalex.org/W6720111944","https://openalex.org/W6721237847","https://openalex.org/W6730623217","https://openalex.org/W6733306783","https://openalex.org/W6734871034","https://openalex.org/W6746282794","https://openalex.org/W6748481559","https://openalex.org/W6748949023","https://openalex.org/W6749954789","https://openalex.org/W6750457364","https://openalex.org/W6752125206","https://openalex.org/W6755196303","https://openalex.org/W6758352129","https://openalex.org/W6767014221","https://openalex.org/W6777331411","https://openalex.org/W6864221053"],"related_works":["https://openalex.org/W3119773509","https://openalex.org/W3174759195","https://openalex.org/W3208297503","https://openalex.org/W2889153461","https://openalex.org/W2964117661","https://openalex.org/W4388405611","https://openalex.org/W3167013339","https://openalex.org/W4287121366","https://openalex.org/W60493759","https://openalex.org/W4308619659"],"abstract_inverted_index":{"Semantic":[0],"segmentation":[1,83,168],"of":[2,24,100,106],"mitochondria":[3],"is":[4],"essential":[5],"for":[6,50],"electron":[7],"microscopy":[8],"image":[9],"analysis.":[10],"Despite":[11],"the":[12,42,51,66,75,90,94,125],"great":[13],"success":[14],"achieved":[15],"using":[16],"supervised":[17],"learning,":[18],"it":[19],"requires":[20],"a":[21,48,104],"large":[22],"amount":[23],"expensive":[25],"per-pixel":[26],"annotations.":[27],"Recent":[28],"studies":[29],"have":[30,146],"proposed":[31],"to":[32,64,74,120],"exploit":[33],"similar":[34],"but":[35,41],"annotated":[36,91],"domains":[37,102],"by":[38,84,89],"domain":[39,45,61,73,108,127],"adaptation,":[40],"possible":[43],"severe":[44],"shift":[46],"poses":[47],"challenge":[49],"model":[52,67],"transfer.":[53],"In":[54],"this":[55],"study,":[56],"we":[57,80,118],"develop":[58],"an":[59,70],"unsupervised":[60],"adaptation":[62],"method":[63,160],"adapt":[65],"trained":[68],"on":[69,149],"labeled":[71],"source":[72,126],"unlabeled":[76],"target":[77],"domain.":[78],"Specifically,":[79],"achieve":[81],"cross-domain":[82],"integrating":[85],"geometrical":[86,122],"cues":[87,96,123],"provided":[88],"labels":[92],"and":[93,132,144,170],"visual":[95,171],"latent":[97],"in":[98,103,167],"images":[99],"both":[101,166],"framework":[105],"adversarial":[107,129],"adaptive":[109],"multi-task":[110],"learning.":[111,130],"Rather":[112],"than":[113],"enforcing":[114],"manually-defined":[115],"shape":[116],"priors,":[117],"propose":[119],"learn":[121],"from":[124],"through":[128,137],"Domain-invariant":[131],"discriminative":[133],"features":[134],"are":[135],"learned":[136],"joint":[138],"adaptation.":[139],"Extensive":[140],"ablations,":[141],"parameter":[142],"analysis":[143],"comparisons":[145],"been":[147],"conducted":[148],"three":[150],"benchmarks":[151],"under":[152],"various":[153],"settings.":[154],"The":[155],"experiments":[156],"show":[157],"that":[158],"our":[159],"performs":[161],"favorably":[162],"against":[163],"state-of-the-art":[164],"methods":[165],"accuracy":[169],"quality.":[172]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":6},{"year":2012,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2020-04-10T00:00:00"}