{"id":"https://openalex.org/W3133630613","doi":"https://doi.org/10.1109/mlsp52302.2021.9596501","title":"Evaluation of Complexity Measures for Deep Learning Generalization in Medical Image Analysis","display_name":"Evaluation of Complexity Measures for Deep Learning Generalization in Medical Image Analysis","publication_year":2021,"publication_date":"2021-10-25","ids":{"openalex":"https://openalex.org/W3133630613","doi":"https://doi.org/10.1109/mlsp52302.2021.9596501","mag":"3133630613","pmid":"https://pubmed.ncbi.nlm.nih.gov/35527797"},"language":"en","primary_location":{"id":"doi:10.1109/mlsp52302.2021.9596501","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp52302.2021.9596501","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2103.03328","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5005267824","display_name":"Aleksandar Vakanski","orcid":"https://orcid.org/0000-0003-3365-1291"},"institutions":[{"id":"https://openalex.org/I155093810","display_name":"University of Idaho","ror":"https://ror.org/03hbp5t65","country_code":"US","type":"education","lineage":["https://openalex.org/I155093810"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Aleksandar Vakanski","raw_affiliation_strings":["University of Idaho,Department of Nuclear Engineering and Industrial Management,Idaho Falls,USA"],"affiliations":[{"raw_affiliation_string":"University of Idaho,Department of Nuclear Engineering and Industrial Management,Idaho Falls,USA","institution_ids":["https://openalex.org/I155093810"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080652935","display_name":"Min Xian","orcid":"https://orcid.org/0000-0001-6098-4441"},"institutions":[{"id":"https://openalex.org/I155093810","display_name":"University of Idaho","ror":"https://ror.org/03hbp5t65","country_code":"US","type":"education","lineage":["https://openalex.org/I155093810"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Min Xian","raw_affiliation_strings":["University of Idaho,Department of Computer Science,Idaho Falls,USA","University of Idaho"],"affiliations":[{"raw_affiliation_string":"University of Idaho,Department of Computer Science,Idaho Falls,USA","institution_ids":["https://openalex.org/I155093810"]},{"raw_affiliation_string":"University of Idaho","institution_ids":["https://openalex.org/I155093810"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5005267824"],"corresponding_institution_ids":["https://openalex.org/I155093810"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.02975096,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"2021","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9994000196456909,"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":0.9994000196456909,"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":0.9976000189781189,"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":0.9966999888420105,"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/generalization","display_name":"Generalization","score":0.7709527015686035},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7203265428543091},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6726726293563843},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6208804845809937},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6064622402191162},{"id":"https://openalex.org/keywords/empirical-research","display_name":"Empirical research","score":0.4327920377254486},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.32766032218933105},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2093093991279602},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.10716092586517334}],"concepts":[{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.7709527015686035},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7203265428543091},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6726726293563843},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6208804845809937},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6064622402191162},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.4327920377254486},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32766032218933105},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2093093991279602},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.10716092586517334},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":7,"locations":[{"id":"doi:10.1109/mlsp52302.2021.9596501","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp52302.2021.9596501","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"},{"id":"pmid:35527797","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/35527797","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE International Workshop on Machine Learning for Signal Processing : [proceedings]. IEEE International Workshop on Machine Learning for Signal Processing","raw_type":null},{"id":"pmh:oai:arXiv.org:2103.03328","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2103.03328","pdf_url":"https://arxiv.org/pdf/2103.03328","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"},{"id":"mag:3133630613","is_oa":true,"landing_page_url":"http://arxiv.org/pdf/2103.03328.pdf","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"pmh:oai:pubmedcentral.nih.gov:9071170","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9071170","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Int Workshop Mach Learn Signal Process","raw_type":"Text"},{"id":"doi:10.48550/arxiv.2103.03328","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2103.03328","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"},{"id":"doi:10.17023/y9b7-ec39","is_oa":true,"landing_page_url":"https://doi.org/10.17023/y9b7-ec39","pdf_url":null,"source":{"id":"https://openalex.org/S7407051697","display_name":"IEEE RESOURCE CENTERS","issn_l":null,"issn":[],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2103.03328","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2103.03328","pdf_url":"https://arxiv.org/pdf/2103.03328","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":[],"awards":[{"id":"https://openalex.org/G5786885734","display_name":null,"funder_award_id":"P20GM104420","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"}],"funders":[{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W1799366690","https://openalex.org/W2029029543","https://openalex.org/W2029538739","https://openalex.org/W2744692634","https://openalex.org/W2912260645","https://openalex.org/W2918775908","https://openalex.org/W2953494151","https://openalex.org/W2963236897","https://openalex.org/W2963285844","https://openalex.org/W2963372435","https://openalex.org/W2963509076","https://openalex.org/W2963534466","https://openalex.org/W2963664410","https://openalex.org/W2963739978","https://openalex.org/W2963959597","https://openalex.org/W2964313743","https://openalex.org/W2967732766","https://openalex.org/W2970290137","https://openalex.org/W2974888503","https://openalex.org/W2991372685","https://openalex.org/W2994848047","https://openalex.org/W3092624683","https://openalex.org/W3096117505","https://openalex.org/W3103089845","https://openalex.org/W3106904274","https://openalex.org/W3112176259","https://openalex.org/W3112948183","https://openalex.org/W3137695714","https://openalex.org/W4238284510","https://openalex.org/W6726983090","https://openalex.org/W6735544424","https://openalex.org/W6739659843","https://openalex.org/W6740483536","https://openalex.org/W6741653254","https://openalex.org/W6747380770","https://openalex.org/W6748230616","https://openalex.org/W6748600614","https://openalex.org/W6758432988","https://openalex.org/W6759238893","https://openalex.org/W6763485134","https://openalex.org/W6766464816","https://openalex.org/W6771233102","https://openalex.org/W6786179248","https://openalex.org/W6787275933"],"related_works":["https://openalex.org/W3211447114","https://openalex.org/W2124678650","https://openalex.org/W3177677752","https://openalex.org/W2802138567","https://openalex.org/W2972321490","https://openalex.org/W1599440649","https://openalex.org/W3204662436","https://openalex.org/W2981850339","https://openalex.org/W3153334740","https://openalex.org/W3149811219","https://openalex.org/W3021669159","https://openalex.org/W3180713997","https://openalex.org/W3115774063","https://openalex.org/W3127320920","https://openalex.org/W3133164560","https://openalex.org/W3036681819","https://openalex.org/W3109867714","https://openalex.org/W3021068338","https://openalex.org/W2809415789","https://openalex.org/W2899682268"],"abstract_inverted_index":{"The":[0,111],"generalization":[1,32,51,67,101],"error":[2],"of":[3,30,103,129],"deep":[4,104],"learning":[5,105],"models":[6,130],"for":[7,19,39,107,126,142],"medical":[8],"image":[9,83],"analysis":[10],"often":[11],"increases":[12],"on":[13,34,79],"images":[14,36,144],"collected":[15],"with":[16,81],"different":[17],"devices":[18],"data":[20],"acquisition,":[21],"device":[22],"settings,":[23],"or":[24],"patient":[25],"population.":[26],"A":[27],"better":[28],"understanding":[29],"the":[31,63,93,100,122,127],"capacity":[33],"new":[35],"is":[37,57,145],"crucial":[38],"clinicians'":[40],"trustworthiness.":[41],"Although":[42],"significant":[43,60],"efforts":[44],"have":[45,75],"been":[46,76],"recently":[47],"directed":[48],"toward":[49,147],"establishing":[50],"bounds":[52],"and":[53,65,99,117,131,139],"complexity":[54,97],"measures,":[55],"there":[56],"still":[58],"a":[59],"discrepancy":[61],"between":[62,95],"predicted":[64],"actual":[66],"performance.":[68],"As":[69],"well,":[70],"related":[71],"large":[72],"empirical":[73,89],"studies":[74],"primarily":[77],"based":[78],"validation":[80],"general-purpose":[82],"datasets.":[84],"This":[85],"paper":[86],"presents":[87],"an":[88],"study":[90],"that":[91,114,136],"investigates":[92],"correlation":[94],"25":[96],"measures":[98,120],"abilities":[102],"classifiers":[106],"breast":[108,143],"ultrasound":[109],"images.":[110],"results":[112],"indicate":[113],"PAC-Bayes":[115],"flatness":[116],"path":[118],"norm":[119],"produce":[121],"most":[123],"consistent":[124],"explanation":[125],"combination":[128],"data.":[132],"We":[133],"also":[134],"report":[135],"multi-task":[137],"classification":[138],"segmentation":[140],"approach":[141],"conducive":[146],"improved":[148],"generalization.":[149]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}
