{"id":"https://openalex.org/W4387430798","doi":"https://doi.org/10.48550/arxiv.2310.03664","title":"Certification of Deep Learning Models for Medical Image Segmentation","display_name":"Certification of Deep Learning Models for Medical Image Segmentation","publication_year":2023,"publication_date":"2023-10-05","ids":{"openalex":"https://openalex.org/W4387430798","doi":"https://doi.org/10.48550/arxiv.2310.03664"},"language":"en","primary_location":{"id":"doi:10.48550/arxiv.2310.03664","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2310.03664","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":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2310.03664","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5010199632","display_name":"Othmane Laousy","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Laousy, Othmane","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053106735","display_name":"Alexandre Araujo","orcid":"https://orcid.org/0000-0003-2220-5739"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Araujo, Alexandre","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016194351","display_name":"Guillaume Chassagnon","orcid":"https://orcid.org/0000-0003-1487-7922"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chassagnon, Guillaume","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077223134","display_name":"Nikos Paragios","orcid":"https://orcid.org/0000-0002-9668-4763"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Paragios, Nikos","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101429507","display_name":"Marie-Pierre Revel","orcid":"https://orcid.org/0000-0001-7906-6499"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Revel, Marie-Pierre","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5106466395","display_name":"Maria Vakalopoulou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vakalopoulou, Maria","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9969000220298767,"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"}},"topics":[{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9969000220298767,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9950000047683716,"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/T14510","display_name":"Medical Imaging and Analysis","score":0.9797000288963318,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.7747721672058105},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.763864278793335},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6891764998435974},{"id":"https://openalex.org/keywords/certification","display_name":"Certification","score":0.6418734192848206},{"id":"https://openalex.org/keywords/quantile","display_name":"Quantile","score":0.5725787878036499},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.557278573513031},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.48859959840774536},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.47541505098342896},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4703407883644104},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.42829108238220215},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3008534908294678},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.10244551301002502},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09496763348579407}],"concepts":[{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.7747721672058105},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.763864278793335},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6891764998435974},{"id":"https://openalex.org/C46304622","wikidata":"https://www.wikidata.org/wiki/Q374814","display_name":"Certification","level":2,"score":0.6418734192848206},{"id":"https://openalex.org/C118671147","wikidata":"https://www.wikidata.org/wiki/Q578714","display_name":"Quantile","level":2,"score":0.5725787878036499},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.557278573513031},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.48859959840774536},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.47541505098342896},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4703407883644104},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.42829108238220215},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3008534908294678},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.10244551301002502},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09496763348579407},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2310.03664","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2310.03664","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2310.03664","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2310.03664","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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2044551864","https://openalex.org/W1572557500","https://openalex.org/W4390690393","https://openalex.org/W3124946120","https://openalex.org/W2047938026","https://openalex.org/W2585269888","https://openalex.org/W3132003399","https://openalex.org/W2005776175","https://openalex.org/W4293365552","https://openalex.org/W4288586621"],"abstract_inverted_index":{"In":[0,34,65],"medical":[1,79,149],"imaging,":[2],"segmentation":[3,27,76,151],"models":[4,28,60,99],"have":[5],"known":[6],"a":[7,35,56,74,160],"significant":[8],"improvement":[9],"in":[10,19,165],"the":[11,45,71,93,103,144],"past":[12],"decade":[13],"and":[14,54,61,85,121,123,153,168],"are":[15,29,128],"now":[16],"used":[17],"daily":[18],"clinical":[20],"practice.":[21],"However,":[22],"similar":[23],"to":[24,58,130,147,158],"classification":[25],"models,":[26,152],"affected":[30],"by":[31],"adversarial":[32],"attacks.":[33],"safety-critical":[36],"field":[37],"like":[38],"healthcare,":[39],"certifying":[40],"model":[41],"predictions":[42],"is":[43],"of":[44,95,105,116],"utmost":[46],"importance.":[47],"Randomized":[48],"smoothing":[49,84],"has":[50],"been":[51],"introduced":[52],"lately":[53],"provides":[55],"framework":[57],"certify":[59,148],"obtain":[62],"theoretical":[63],"guarantees.":[64],"this":[66,166],"paper,":[67],"we":[68,127,154],"present":[69],"for":[70,78,137,156,162],"first":[72,145],"time":[73],"certified":[75,133],"baseline":[77],"imaging":[80],"based":[81],"on":[82,112],"randomized":[83,106],"diffusion":[86,97],"models.":[87],"Our":[88,141],"results":[89],"show":[90,125],"that":[91,126],"leveraging":[92],"power":[94],"denoising":[96],"probabilistic":[98],"helps":[100],"us":[101],"overcome":[102],"limits":[104],"smoothing.":[107],"We":[108],"conduct":[109],"extensive":[110],"experiments":[111],"five":[113],"public":[114],"datasets":[115],"chest":[117],"X-rays,":[118],"skin":[119],"lesions,":[120],"colonoscopies,":[122],"empirically":[124],"able":[129],"maintain":[131],"high":[132],"Dice":[134],"scores":[135],"even":[136],"highly":[138],"perturbed":[139],"images.":[140],"work":[142],"represents":[143],"attempt":[146],"image":[150],"aspire":[155],"it":[157],"set":[159],"foundation":[161],"future":[163],"benchmarks":[164],"crucial":[167],"largely":[169],"uncharted":[170],"area.":[171]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2025-10-10T00:00:00"}