{"id":"https://openalex.org/W4321524315","doi":"https://doi.org/10.48550/arxiv.2302.10185","title":"Active Learning in Brain Tumor Segmentation with Uncertainty Sampling, Annotation Redundancy Restriction, and Data Initialization","display_name":"Active Learning in Brain Tumor Segmentation with Uncertainty Sampling, Annotation Redundancy Restriction, and Data Initialization","publication_year":2023,"publication_date":"2023-02-05","ids":{"openalex":"https://openalex.org/W4321524315","doi":"https://doi.org/10.48550/arxiv.2302.10185"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2302.10185","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2302.10185","pdf_url":"https://arxiv.org/pdf/2302.10185","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/2302.10185","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103071979","display_name":"Daniel D Kim","orcid":"https://orcid.org/0000-0003-0731-8559"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Kim, Daniel D","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034602999","display_name":"Rajat Chandra","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chandra, Rajat S","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101545679","display_name":"Jian Peng","orcid":"https://orcid.org/0000-0003-4269-4189"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peng, Jian","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100599432","display_name":"Jing Wu","orcid":"https://orcid.org/0000-0001-8619-566X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Jing","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036168675","display_name":"Xue Feng","orcid":"https://orcid.org/0000-0002-9057-1549"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feng, Xue","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090645124","display_name":"Michael K. Atalay","orcid":"https://orcid.org/0000-0003-3549-2634"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Atalay, Michael","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069558252","display_name":"Chetan Bettegowda","orcid":"https://orcid.org/0000-0001-9991-7123"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bettegowda, Chetan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018714925","display_name":"Craig Jones","orcid":"https://orcid.org/0000-0002-0629-3006"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jones, Craig","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070419330","display_name":"Haris I. Sair","orcid":"https://orcid.org/0000-0002-5077-0471"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sair, Haris","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033945250","display_name":"Weihua Liao","orcid":"https://orcid.org/0000-0002-9168-422X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liao, Wei-hua","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001698371","display_name":"Chengzhang Zhu","orcid":"https://orcid.org/0000-0003-0495-1981"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Chengzhang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062015169","display_name":"Beiji Zou","orcid":"https://orcid.org/0000-0002-3542-1097"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zou, Beiji","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100319866","display_name":"Li Yang","orcid":"https://orcid.org/0000-0002-9337-1382"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Li","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000168473","display_name":"Anahita Fathi Kazerooni","orcid":"https://orcid.org/0000-0001-7131-2261"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kazerooni, Anahita Fathi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079259769","display_name":"Ali Nabavizadeh","orcid":"https://orcid.org/0000-0002-0380-4552"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nabavizadeh, Ali","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080679036","display_name":"Harrison X. Bai","orcid":"https://orcid.org/0000-0002-7460-8866"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bai, Harrison X","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5052716839","display_name":"Zhicheng Jiao","orcid":"https://orcid.org/0000-0002-6968-0919"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiao, Zhicheng","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":17,"corresponding_author_ids":["https://openalex.org/A5103071979"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":1,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.972599983215332,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.972599983215332,"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9537000060081482,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.9362999796867371,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/initialization","display_name":"Initialization","score":0.7195412516593933},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.6634762287139893},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6329413652420044},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.622208297252655},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.581976056098938},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.533155083656311},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3867010474205017},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36365681886672974}],"concepts":[{"id":"https://openalex.org/C114466953","wikidata":"https://www.wikidata.org/wiki/Q6034165","display_name":"Initialization","level":2,"score":0.7195412516593933},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.6634762287139893},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6329413652420044},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.622208297252655},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.581976056098938},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.533155083656311},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3867010474205017},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36365681886672974},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2302.10185","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2302.10185","pdf_url":"https://arxiv.org/pdf/2302.10185","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.2302.10185","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2302.10185","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:2302.10185","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2302.10185","pdf_url":"https://arxiv.org/pdf/2302.10185","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":[],"awards":[{"id":"https://openalex.org/G4066238608","display_name":null,"funder_award_id":"B18059","funder_id":"https://openalex.org/F4320327912","funder_display_name":"Higher Education Discipline Innovation Project"},{"id":"https://openalex.org/G6755165505","display_name":null,"funder_award_id":"award","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G848032724","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320327912","display_name":"Higher Education Discipline Innovation Project","ror":null}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4321524315.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W3204184292","https://openalex.org/W3176564347","https://openalex.org/W1985458517","https://openalex.org/W2355833770","https://openalex.org/W3031039437","https://openalex.org/W183202219","https://openalex.org/W3095877357","https://openalex.org/W2072565696","https://openalex.org/W2050451745","https://openalex.org/W2378903222"],"abstract_inverted_index":{"Deep":[0],"learning":[1,26],"models":[2],"have":[3],"demonstrated":[4],"great":[5],"potential":[6],"in":[7],"medical":[8],"3D":[9,76],"imaging,":[10],"but":[11,237],"their":[12],"development":[13],"is":[14,164],"limited":[15],"by":[16,30,117],"the":[17,38,57,122,131,142,146,160,181,190,204,219,257],"expensive,":[18],"large":[19],"volume":[20],"of":[21,37,59,134,180,189,203,218,232],"annotated":[22,259],"data":[23,41,60,135,183,192,206],"required.":[24],"Active":[25],"(AL)":[27],"addresses":[28],"this":[29],"training":[31,162,172,191,205,220],"a":[32,35,53,75],"model":[33,78,143,184],"on":[34,145],"subset":[36,124],"most":[39],"informative":[40],"samples":[42],"without":[43],"compromising":[44],"performance.":[45,64],"We":[46,83,129],"compared":[47,108,194],"different":[48],"AL":[49,81,247],"strategies":[50,248],"and":[51,79,90,104,173,234],"propose":[52],"framework":[54],"that":[55,179],"minimizes":[56],"amount":[58,133],"needed":[61],"for":[62],"state-of-the-art":[63,213,254],"638":[65],"multi-institutional":[66],"brain":[67],"tumor":[68],"MRI":[69],"images":[70,120],"were":[71,107,125,239],"used":[72],"to":[73,109,113,137,141,158,178,195],"train":[74],"U-net":[77],"compare":[80],"strategies.":[82],"investigated":[84,245],"uncertainty":[85,251],"sampling,":[86],"annotation":[87,115],"redundancy":[88,116,209],"restriction,":[89],"initial":[91,161,229],"dataset":[92,148,163,223,230],"selection":[93,154],"techniques.":[94],"Uncertainty":[95],"estimation":[96,100,252],"techniques":[97,211],"including":[98],"Bayesian":[99,167],"with":[101,169,185,228,249,256],"dropout,":[102],"bootstrapping,":[103],"margins":[105],"sampling":[106],"random":[110,196],"query.":[111],"Strategies":[112],"avoid":[114],"removing":[118],"similar":[119,139,176,199],"within":[121],"to-be-annotated":[123],"considered":[126],"as":[127],"well.":[128],"determined":[130],"minimum":[132],"necessary":[136],"achieve":[138],"performance":[140,200,214,255],"trained":[144],"full":[147,182],"(\u03b1":[149],"=":[150],"0.1).":[151],"A":[152],"variance-based":[153],"strategy":[155],"using":[156],"radiomics":[157],"identify":[159],"also":[165],"proposed.":[166],"approximation":[168],"dropout":[170,250],"at":[171,201,215],"testing":[174],"showed":[175],"results":[177],"less":[186],"than":[187],"20%":[188],"(p=0.293)":[193],"query":[197],"achieving":[198,253],"56.5%":[202],"(p=0.814).":[207],"Annotation":[208],"restriction":[210],"achieved":[212],"approximately":[216],"40%-50%":[217],"data.":[221,260],"Radiomics":[222],"initialization":[224],"had":[225],"higher":[226],"Dice":[227],"sizes":[231],"20":[233],"80":[235],"images,":[236],"improvements":[238],"not":[240],"significant.":[241],"In":[242],"conclusion,":[243],"we":[244],"various":[246],"least":[258]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2023-02-24T00:00:00"}