{"id":"https://openalex.org/W4401750065","doi":"https://doi.org/10.1109/isbi56570.2024.10635145","title":"Deep Asymmetric Mixture Model for Unsupervised Cell Segmentation","display_name":"Deep Asymmetric Mixture Model for Unsupervised Cell Segmentation","publication_year":2024,"publication_date":"2024-05-27","ids":{"openalex":"https://openalex.org/W4401750065","doi":"https://doi.org/10.1109/isbi56570.2024.10635145"},"language":"en","primary_location":{"id":"doi:10.1109/isbi56570.2024.10635145","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/isbi56570.2024.10635145","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Symposium on Biomedical Imaging (ISBI)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5114188716","display_name":"Yang Nan","orcid":null},"institutions":[{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"education","lineage":["https://openalex.org/I47508984"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Yang Nan","raw_affiliation_strings":["Imperial College London Imperial-X, Imperial College,Department of Bioengineering,London"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Imperial College London Imperial-X, Imperial College,Department of Bioengineering,London","institution_ids":["https://openalex.org/I47508984"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100436460","display_name":"Guang Yang","orcid":"https://orcid.org/0000-0001-7344-7733"},"institutions":[{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"education","lineage":["https://openalex.org/I47508984"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Guang Yang","raw_affiliation_strings":["Imperial College London Imperial-X, Imperial College,Department of Bioengineering,London"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Imperial College London Imperial-X, Imperial College,Department of Bioengineering,London","institution_ids":["https://openalex.org/I47508984"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I47508984"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.12643449,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9983000159263611,"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":0.9983000159263611,"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":0.9973999857902527,"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"}},{"id":"https://openalex.org/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.9973999857902527,"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/computer-science","display_name":"Computer science","score":0.6391830444335938},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6073755025863647},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5426467061042786},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.41383570432662964},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.345145583152771}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6391830444335938},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6073755025863647},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5426467061042786},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.41383570432662964},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.345145583152771}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isbi56570.2024.10635145","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/isbi56570.2024.10635145","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Symposium on Biomedical Imaging (ISBI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W2056116011","https://openalex.org/W2080860745","https://openalex.org/W2112934038","https://openalex.org/W2148464528","https://openalex.org/W2894257256","https://openalex.org/W2982083293","https://openalex.org/W2990500698","https://openalex.org/W3008699835","https://openalex.org/W3042615550","https://openalex.org/W3099193570","https://openalex.org/W3210550184","https://openalex.org/W4288751099","https://openalex.org/W4390874575","https://openalex.org/W6748102297","https://openalex.org/W6754670368"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W2033914206","https://openalex.org/W2042327336","https://openalex.org/W1522196789"],"abstract_inverted_index":{"Automated":[0],"cell":[1,101,142],"segmentation":[2,33,143],"has":[3],"become":[4],"increasingly":[5],"crucial":[6],"for":[7,69,99],"disease":[8],"diagnosis":[9],"and":[10,19,82,118],"drug":[11],"discovery,":[12],"as":[13],"manual":[14,27],"delineation":[15],"is":[16,67,72,107],"excessively":[17],"laborious":[18],"subjective.":[20],"To":[21,87],"address":[22,88],"this":[23,91],"issue":[24],"with":[25,116],"limited":[26],"annotation,":[28],"researchers":[29],"have":[30],"developed":[31],"semi/unsupervised":[32],"approaches.":[34],"Among":[35],"these":[36,56,89],"approaches,":[37],"Deep":[38],"Gaussian":[39,113],"mixture":[40,97,105,114,126],"model":[41,98,106,127],"plays":[42],"a":[43,94],"vital":[44],"role":[45],"due":[46],"to":[47,50,85],"its":[48],"capacity":[49,81],"facilitate":[51],"complex":[52],"data":[53,61,70],"distributions.":[54],"However,":[55],"models":[57,76,115,140],"assume":[58],"that":[59,71],"the":[60,136,145],"follows":[62],"symmetric":[63],"normal":[64],"distributions,":[65],"which":[66],"inapplicable":[68],"asymmetrically":[73],"distributed.":[74],"These":[75],"also":[77],"obstacles":[78],"weak":[79],"generalization":[80],"are":[83],"sensitive":[84],"outliers.":[86],"issues,":[90],"paper":[92],"presents":[93],"novel":[95],"asymmetric":[96,104,125],"unsupervised":[100,139],"segmentation.":[102],"This":[103],"built":[108],"by":[109],"aggregating":[110],"certain":[111],"multivariate":[112],"log-likelihood":[117],"self-supervised":[119],"based":[120],"optimization":[121],"functions.":[122],"The":[123],"proposed":[124],"outperforms":[128],"(nearly":[129],"2-30%":[130],"gain":[131],"in":[132],"dice":[133],"coefficient,":[134],"p<0.05)":[135],"existing":[137],"state-of-the-art":[138],"on":[141],"including":[144],"segment":[146],"anything.":[147]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
