{"id":"https://openalex.org/W4388748074","doi":"https://doi.org/10.48550/arxiv.2311.08774","title":"Two-stage Joint Transductive and Inductive learning for Nuclei Segmentation","display_name":"Two-stage Joint Transductive and Inductive learning for Nuclei Segmentation","publication_year":2023,"publication_date":"2023-11-15","ids":{"openalex":"https://openalex.org/W4388748074","doi":"https://doi.org/10.48550/arxiv.2311.08774"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2311.08774","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2311.08774","pdf_url":"https://arxiv.org/pdf/2311.08774","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2311.08774","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5047703641","display_name":"Hesham Ali","orcid":"https://orcid.org/0000-0001-6675-7987"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ali, Hesham","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093278335","display_name":"Idriss Cabrel Tsewalo Tondji","orcid":"https://orcid.org/0009-0002-6014-4199"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tondji, Idriss","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5022335103","display_name":"Mennatullah Siam","orcid":"https://orcid.org/0000-0003-1854-3698"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Siam, Mennatullah","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5047703641"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9991999864578247,"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":0.9991999864578247,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9901999831199646,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9887999892234802,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7627277374267578},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7281068563461304},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.727574348449707},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7138848900794983},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6151282787322998},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.595602810382843},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5451048016548157},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.49753621220588684},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.48578789830207825},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.4741886556148529},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4391811788082123},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.07222285866737366}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7627277374267578},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7281068563461304},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.727574348449707},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7138848900794983},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6151282787322998},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.595602810382843},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5451048016548157},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.49753621220588684},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.48578789830207825},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.4741886556148529},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4391811788082123},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.07222285866737366},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","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/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","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/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2311.08774","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2311.08774","pdf_url":"https://arxiv.org/pdf/2311.08774","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2311.08774","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2311.08774","pdf_url":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2311.08774","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2311.08774","pdf_url":"https://arxiv.org/pdf/2311.08774","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.6200000047683716,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W2745001401","https://openalex.org/W4321353415","https://openalex.org/W2130974462","https://openalex.org/W2028665553","https://openalex.org/W2086519370","https://openalex.org/W972276598","https://openalex.org/W2087343574","https://openalex.org/W4246352526","https://openalex.org/W2121910908"],"abstract_inverted_index":{"AI-assisted":[0],"nuclei":[1,69],"segmentation":[2,70],"in":[3,10,43],"histopathological":[4],"images":[5,28],"is":[6,52,138],"a":[7,45,53,65,98,145,155],"crucial":[8],"task":[9],"the":[11,20,32,73,83,106,119,123,129,132,139,170],"diagnosis":[12],"and":[13,29,76,88,109,172],"treatment":[14],"of":[15,49,85,121,134,174],"cancer":[16],"diseases.":[17],"It":[18],"decreases":[19],"time":[21],"required":[22],"to":[23,68,111,127,142,168],"manually":[24],"screen":[25],"microscopic":[26],"tissue":[27],"can":[30],"resolve":[31],"conflict":[33],"between":[34],"pathologists":[35],"during":[36],"diagnosis.":[37],"Deep":[38],"Learning":[39],"has":[40,118],"proven":[41],"useful":[42],"such":[44,144],"task.":[46],"However,":[47],"lack":[48],"labeled":[50],"data":[51,126],"significant":[54],"barrier":[55],"for":[56,148],"deep":[57],"learning-based":[58],"approaches.":[59],"In":[60],"this":[61,137],"study,":[62],"we":[63,153],"propose":[64,143,154],"novel":[66,156],"approach":[67,147,164],"that":[71],"leverages":[72],"available":[74],"labelled":[75],"unlabelled":[77,124],"data.":[78],"The":[79],"proposed":[80],"method":[81],"combines":[82],"strengths":[84],"both":[86],"transductive":[87,116,158],"inductive":[89],"learning,":[90],"which":[91],"have":[92],"been":[93],"previously":[94],"attempted":[95],"separately,":[96],"into":[97],"single":[99],"framework.":[100],"Inductive":[101],"learning":[102,117],"aims":[103],"at":[104],"approximating":[105],"general":[107],"function":[108],"generalizing":[110],"unseen":[112],"test":[113,125],"data,":[114],"while":[115],"potential":[120,173],"leveraging":[122],"improve":[128],"classification.":[130],"To":[131],"best":[133],"our":[135,163,175],"knowledge,":[136],"first":[140],"study":[141],"hybrid":[146],"medical":[149],"image":[150],"segmentation.":[151],"Moreover,":[152],"two-stage":[157],"inference":[159],"scheme.":[160],"We":[161],"evaluate":[162],"on":[165],"MoNuSeg":[166],"benchmark":[167],"demonstrate":[169],"efficacy":[171],"method.":[176]},"counts_by_year":[],"updated_date":"2026-02-09T09:26:11.010843","created_date":"2025-10-10T00:00:00"}