{"id":"https://openalex.org/W7155192089","doi":"https://doi.org/10.48550/arxiv.2604.19323","title":"Concept Inconsistency in Dermoscopic Concept Bottleneck Models: A Rough-Set Analysis of the Derm7pt Dataset","display_name":"Concept Inconsistency in Dermoscopic Concept Bottleneck Models: A Rough-Set Analysis of the Derm7pt Dataset","publication_year":2026,"publication_date":"2026-04-21","ids":{"openalex":"https://openalex.org/W7155192089","doi":"https://doi.org/10.48550/arxiv.2604.19323"},"language":"en","primary_location":{"id":"pmh:oai:pure.tue.nl:openaire/d0c5ba9e-9e34-4ae7-b998-debe2aaeb5f3","is_oa":false,"landing_page_url":"https://research.tue.nl/en/publications/d0c5ba9e-9e34-4ae7-b998-debe2aaeb5f3","pdf_url":null,"source":{"id":"https://openalex.org/S4406922641","display_name":"TU/e Research Portal","issn_l":null,"issn":null,"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"N\u00e1poles, G, Grau, I & Salgueiro, Y 2026 'Concept Inconsistency in Dermoscopic Concept Bottleneck Models : A Rough-Set Analysis of the Derm7pt Dataset' arXiv.org. https://doi.org/10.48550/arXiv.2604.19323","raw_type":"info:eu-repo/semantics/preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.19323","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134269246","display_name":"Gonzalo N\u00e1poles","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"N\u00e1poles, Gonzalo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079498248","display_name":"Isel Grau","orcid":"https://orcid.org/0000-0002-8035-2887"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Grau, Isel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5122042098","display_name":"Yamisleydi Salgueiro","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Salgueiro, Yamisleydi","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/T10392","display_name":"Cutaneous Melanoma Detection and Management","score":0.9235000014305115,"subfield":{"id":"https://openalex.org/subfields/2730","display_name":"Oncology"},"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/T10392","display_name":"Cutaneous Melanoma Detection and Management","score":0.9235000014305115,"subfield":{"id":"https://openalex.org/subfields/2730","display_name":"Oncology"},"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/T10862","display_name":"AI in cancer detection","score":0.012199999764561653,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.00930000003427267,"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/interpretability","display_name":"Interpretability","score":0.891700029373169},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.7547000050544739},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5480999946594238},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.45559999346733093},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4187000095844269},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.30979999899864197},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.30070000886917114}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.891700029373169},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.7547000050544739},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6371999979019165},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5663999915122986},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5480999946594238},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.45559999346733093},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.44290000200271606},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4388999938964844},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4187000095844269},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.30979999899864197},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.30070000886917114},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.290800005197525},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.2766000032424927},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.27149999141693115},{"id":"https://openalex.org/C2777489069","wikidata":"https://www.wikidata.org/wiki/Q1589822","display_name":"Ceiling (cloud)","level":2,"score":0.26649999618530273},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.2590000033378601},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.25870001316070557}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:pure.tue.nl:openaire/d0c5ba9e-9e34-4ae7-b998-debe2aaeb5f3","is_oa":false,"landing_page_url":"https://research.tue.nl/en/publications/d0c5ba9e-9e34-4ae7-b998-debe2aaeb5f3","pdf_url":null,"source":{"id":"https://openalex.org/S4406922641","display_name":"TU/e Research Portal","issn_l":null,"issn":null,"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"N\u00e1poles, G, Grau, I & Salgueiro, Y 2026 'Concept Inconsistency in Dermoscopic Concept Bottleneck Models : A Rough-Set Analysis of the Derm7pt Dataset' arXiv.org. https://doi.org/10.48550/arXiv.2604.19323","raw_type":"info:eu-repo/semantics/preprint"},{"id":"doi:10.48550/arxiv.2604.19323","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.19323","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.19323","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.19323","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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Concept":[0],"Bottleneck":[1],"Models":[2],"(CBMs)":[3],"route":[4],"predictions":[5],"exclusively":[6,113],"through":[7],"a":[8,19,38,97,156,181,222,237],"clinically":[9],"grounded":[10],"concept":[11,25,71,223,244],"layer,":[12],"binding":[13],"interpretability":[14],"to":[15,28,52],"concept-label":[16],"consistency.":[17],"When":[18],"dataset":[20,143],"contains":[21],"concept-level":[22],"inconsistencies,":[23],"identical":[24],"profiles":[26,72],"mapped":[27],"conflicting":[29],"diagnosis":[30],"labels":[31],"create":[32],"an":[33],"unresolvable":[34],"bottleneck":[35],"that":[36,112],"imposes":[37],"hard":[39,116,171,182],"ceiling":[40,100],"on":[41,142,175,216,257],"achievable":[42],"accuracy.":[43],"In":[44,118],"this":[45,66,176],"paper,":[46],"we":[47,120,179],"apply":[48],"rough":[49],"set":[50],"theory":[51],"the":[53,59,75,80,93,122,126,190,206,217],"Derm7pt":[54],"dermoscopy":[55],"benchmark":[56,159],"and":[57,62,134,145,169,194,212,243],"characterize":[58,121],"full":[60],"extent":[61],"clinical":[63,127],"structure":[64],"of":[65,79,92,101,104,150,161,167,225,241,246],"inconsistency.":[67],"Among":[68],"305":[69],"unique":[70],"formed":[73],"by":[74],"7":[76],"dermoscopic":[77,258],"criteria":[78],"7-point":[81],"melanoma":[82],"checklist,":[83],"50":[84],"(16.4%)":[85],"are":[86],"inconsistent,":[87],"spanning":[88],"306":[89],"images":[90,153,163],"(30.3%":[91],"dataset).":[94],"This":[95],"yields":[96,154],"theoretical":[98],"accuracy":[99,172,214,224,245],"92.1%,":[102],"independent":[103],"backbone":[105,187],"architecture":[106],"or":[107],"training":[108],"strategy":[109],"for":[110,131,202,253],"CBMs":[111],"operate":[114],"with":[115,139,164,221],"concepts.":[117],"addition,":[119],"conflict-severity":[123],"distribution,":[124],"identify":[125],"features":[128],"most":[129],"responsible":[130],"boundary":[132],"ambiguity,":[133],"evaluate":[135],"two":[136],"filtering":[137],"strategies":[138],"quantified":[140],"effects":[141],"composition":[144],"CBM":[146,183,255],"interpretability.":[147],"Symmetric":[148],"removal":[149],"all":[151,233],"boundary-region":[152],"Derm7pt+,":[155],"fully":[157],"consistent":[158],"subset":[160],"705":[162],"perfect":[165],"quality":[166],"classification":[168],"no":[170],"ceiling.":[173],"Building":[174],"filtered":[177],"dataset,":[178],"present":[180],"evaluated":[184],"across":[185,232],"19":[186],"architectures":[188],"from":[189],"EfficientNet,":[191],"DenseNet,":[192],"ResNet,":[193],"Wide":[195],"ResNet":[196],"families.":[197],"Under":[198,227],"symmetric":[199],"filtering,":[200,229],"explored":[201],"completeness,":[203],"EfficientNet-B5":[204],"achieves":[205],"best":[207],"label":[208,213,238],"F1":[209,239],"score":[210,240],"(0.85)":[211],"(0.90)":[215],"held-out":[218],"test":[219],"set,":[220],"0.70.":[226,247],"asymmetric":[228],"EfficientNet-B7":[230],"leads":[231],"four":[234],"metrics,":[235],"reaching":[236],"0.82":[242],"These":[248],"results":[249],"establish":[250],"reproducible":[251],"baselines":[252],"concept-consistent":[254],"evaluation":[256],"data.":[259]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-23T00:00:00"}
