{"id":"https://openalex.org/W2963753319","doi":"https://doi.org/10.1109/isbi.2019.8759530","title":"US-Net for Robust and Efficient Nuclei Instance Segmentation","display_name":"US-Net for Robust and Efficient Nuclei Instance Segmentation","publication_year":2019,"publication_date":"2019-04-01","ids":{"openalex":"https://openalex.org/W2963753319","doi":"https://doi.org/10.1109/isbi.2019.8759530","mag":"2963753319"},"language":"en","primary_location":{"id":"doi:10.1109/isbi.2019.8759530","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi.2019.8759530","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)","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/A5038456345","display_name":"Zhaoyang Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I166337079","display_name":"Queen Mary University of London","ror":"https://ror.org/026zzn846","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I166337079"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Zhaoyang Xu","raw_affiliation_strings":["Multimedia and Vision Group (MMV), Queen Mary University of London, United Kingdom"],"affiliations":[{"raw_affiliation_string":"Multimedia and Vision Group (MMV), Queen Mary University of London, United Kingdom","institution_ids":["https://openalex.org/I166337079"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053962426","display_name":"Faranak Sobhani","orcid":"https://orcid.org/0000-0002-1787-4455"},"institutions":[{"id":"https://openalex.org/I166337079","display_name":"Queen Mary University of London","ror":"https://ror.org/026zzn846","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I166337079"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Faranak Sobhani","raw_affiliation_strings":["Multimedia and Vision Group (MMV), Queen Mary University of London, United Kingdom"],"affiliations":[{"raw_affiliation_string":"Multimedia and Vision Group (MMV), Queen Mary University of London, United Kingdom","institution_ids":["https://openalex.org/I166337079"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025483481","display_name":"Carlos Fern\u00e1ndez Moro","orcid":"https://orcid.org/0000-0001-6863-5959"},"institutions":[{"id":"https://openalex.org/I1335113835","display_name":"Karolinska University Hospital","ror":"https://ror.org/00m8d6786","country_code":"SE","type":"healthcare","lineage":["https://openalex.org/I1335113835"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Carlos Fernandez Moro","raw_affiliation_strings":["Dept. of Clinical Pathology/Cytology, Karolinska University Hospital, Stockholm, Sweden"],"affiliations":[{"raw_affiliation_string":"Dept. of Clinical Pathology/Cytology, Karolinska University Hospital, Stockholm, Sweden","institution_ids":["https://openalex.org/I1335113835"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067368513","display_name":"Qianni Zhang","orcid":"https://orcid.org/0000-0001-7685-2187"},"institutions":[{"id":"https://openalex.org/I166337079","display_name":"Queen Mary University of London","ror":"https://ror.org/026zzn846","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I166337079"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Qianni Zhang","raw_affiliation_strings":["Multimedia and Vision Group (MMV), Queen Mary University of London, United Kingdom"],"affiliations":[{"raw_affiliation_string":"Multimedia and Vision Group (MMV), Queen Mary University of London, United Kingdom","institution_ids":["https://openalex.org/I166337079"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5038456345"],"corresponding_institution_ids":["https://openalex.org/I166337079"],"apc_list":null,"apc_paid":null,"fwci":1.1201,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.8372523,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"44","last_page":"47"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9997000098228455,"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":0.9997000098228455,"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/T14510","display_name":"Medical Imaging and Analysis","score":0.9936000108718872,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T10146","display_name":"Cervical Cancer and HPV Research","score":0.9855999946594238,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.8529199361801147},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8067711591720581},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6394687294960022},{"id":"https://openalex.org/keywords/network-architecture","display_name":"Network architecture","score":0.5495612621307373},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.543770968914032},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4857654869556427},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4855203628540039},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.4653646945953369},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4616038203239441},{"id":"https://openalex.org/keywords/segmentation-based-object-categorization","display_name":"Segmentation-based object categorization","score":0.4507203698158264},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3365458846092224},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.10679170489311218}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.8529199361801147},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8067711591720581},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6394687294960022},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.5495612621307373},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.543770968914032},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4857654869556427},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4855203628540039},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.4653646945953369},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4616038203239441},{"id":"https://openalex.org/C25694479","wikidata":"https://www.wikidata.org/wiki/Q7446278","display_name":"Segmentation-based object categorization","level":5,"score":0.4507203698158264},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3365458846092224},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.10679170489311218},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isbi.2019.8759530","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi.2019.8759530","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4399999976158142,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[{"id":"https://openalex.org/G533603882","display_name":null,"funder_award_id":"EP/N034708/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"}],"funders":[{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1522301498","https://openalex.org/W1536680647","https://openalex.org/W1901129140","https://openalex.org/W2031489346","https://openalex.org/W2068730032","https://openalex.org/W2183182206","https://openalex.org/W2248620004","https://openalex.org/W2347064614","https://openalex.org/W2412782625","https://openalex.org/W2522761331","https://openalex.org/W2884561390","https://openalex.org/W2963037989","https://openalex.org/W2964121744","https://openalex.org/W3106250896","https://openalex.org/W6631190155","https://openalex.org/W6639824700","https://openalex.org/W6705012245","https://openalex.org/W6785652829"],"related_works":["https://openalex.org/W3144569342","https://openalex.org/W2185902295","https://openalex.org/W2945274617","https://openalex.org/W2103507220","https://openalex.org/W2055202857","https://openalex.org/W2371519352","https://openalex.org/W4205800335","https://openalex.org/W2386644571","https://openalex.org/W2551987074","https://openalex.org/W2372421320"],"abstract_inverted_index":{"We":[0],"present":[1],"a":[2],"novel":[3],"neural":[4],"network":[5,44,58],"architecture,":[6],"US-Net,":[7],"for":[8],"robust":[9],"nuclei":[10,21,93],"instance":[11],"segmentation":[12,24,57,96],"in":[13,83],"histopathology":[14],"images.":[15],"The":[16,42],"proposed":[17,73],"framework":[18,74],"integrates":[19],"the":[20,31,38,48,56,62,66,77,91],"detection":[22,43,94],"and":[23,35,87,95],"networks":[25,82],"by":[26],"sharing":[27],"their":[28],"outputs":[29],"through":[30],"same":[32],"foundation":[33],"network,":[34],"thus":[36],"enhancing":[37],"performance":[39,78],"of":[40,79,90],"both.":[41],"takes":[45],"into":[46],"account":[47],"high-level":[49],"semantic":[50],"cues":[51],"with":[52],"contextual":[53],"information,":[54],"while":[55],"focuses":[59],"more":[60],"on":[61],"low-level":[63],"details":[64],"like":[65],"edges.":[67],"Extensive":[68],"experiments":[69],"reveal":[70],"that":[71],"our":[72],"can":[75],"strengthen":[76],"both":[80],"branch":[81],"an":[84],"integrated":[85],"architecture":[86],"outperforms":[88],"most":[89],"state-of-the-art":[92],"networks.":[97]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}