{"id":"https://openalex.org/W7128618539","doi":"https://doi.org/10.48550/arxiv.2602.09324","title":"Deep Modeling and Interpretation for Bladder Cancer Classification","display_name":"Deep Modeling and Interpretation for Bladder Cancer Classification","publication_year":2026,"publication_date":"2026-02-10","ids":{"openalex":"https://openalex.org/W7128618539","doi":"https://doi.org/10.48550/arxiv.2602.09324"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.09324","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":null,"raw_type":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5125669862","display_name":"Ahmad Chaddad","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Chaddad, Ahmad","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055916281","display_name":"Yihang Wu","orcid":"https://orcid.org/0000-0002-8640-8650"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Yihang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5125647592","display_name":"Xianrui Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Xianrui","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5125669862"],"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":0.321399986743927,"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":0.321399986743927,"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/T10458","display_name":"Bladder and Urothelial Cancer Treatments","score":0.2621999979019165,"subfield":{"id":"https://openalex.org/subfields/2746","display_name":"Surgery"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.10100000351667404,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.8278999924659729},{"id":"https://openalex.org/keywords/bladder-cancer","display_name":"Bladder cancer","score":0.699400007724762},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5920000076293945},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.49559998512268066},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.41029998660087585},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4027000069618225}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8278999924659729},{"id":"https://openalex.org/C2780352672","wikidata":"https://www.wikidata.org/wiki/Q504775","display_name":"Bladder cancer","level":3,"score":0.699400007724762},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6805999875068665},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5920000076293945},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.503000020980835},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.49559998512268066},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.41029998660087585},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4027000069618225},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.38989999890327454},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35580000281333923},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35519999265670776},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.32690000534057617},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.303600013256073},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.27459999918937683},{"id":"https://openalex.org/C40993552","wikidata":"https://www.wikidata.org/wiki/Q514654","display_name":"Gold standard (test)","level":2,"score":0.27410000562667847},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.25619998574256897}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.09324","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.09324","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.09324","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"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.09324","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":null,"raw_type":"Article"},"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":{"Deep":[0],"models":[1,21,50,70,84,103,164],"based":[2],"on":[3,16,112],"vision":[4],"transformer":[5,153],"(ViT)":[6],"and":[7,73,92,119,151,187],"convolutional":[8],"neural":[9],"network":[10],"(CNN)":[11],"have":[12],"demonstrated":[13],"remarkable":[14],"performance":[15],"natural":[17],"datasets.":[18],"However,":[19],"these":[20,62,83,102],"may":[22],"not":[23],"be":[24],"similar":[25],"in":[26],"medical":[27],"imaging,":[28],"where":[29],"abnormal":[30],"regions":[31],"cover":[32],"only":[33],"a":[34,113,170,174],"small":[35],"portion":[36],"of":[37,101],"the":[38,47,58,99,120,125,163],"image.":[39],"This":[40],"challenge":[41],"motivates":[42],"this":[43],"study":[44],"to":[45,60,80,97,132,149,161],"investigate":[46],"latest":[48],"deep":[49,63],"for":[51,88,104,173,182,192],"bladder":[52,89,116,134],"cancer":[53,90,117,135],"classification":[54,67],"tasks.":[55],"We":[56,107,155],"propose":[57],"following":[59],"evaluate":[61,98],"models:":[64],"1)":[65],"standard":[66],"using":[68],"13":[69],"(four":[71],"CNNs":[72],"eight":[74],"transormer-based":[75],"models),":[76],"2)":[77],"calibration":[78,146],"analysis":[79],"examine":[81],"if":[82],"are":[85,180,190],"well":[86],"calibrated":[87],"classification,":[91],"3)":[93],"we":[94],"use":[95],"GradCAM++":[96],"interpretability":[100],"clinical":[105],"diagnosis.":[106],"simulate":[108],"$\\sim":[109,138],"300$":[110],"experiments":[111],"publicly":[114],"multicenter":[115],"dataset,":[118],"experimental":[121],"results":[122],"demonstrate":[123],"that":[124],"ConvNext":[126,150,178],"series":[127,179],"indicate":[128],"limited":[129],"generalization":[130],"ability":[131],"classify":[133],"images":[136],"(e.g.,":[137],"60\\%$":[139],"accuracy).":[140],"In":[141],"addition,":[142],"ViTs":[143],"show":[144],"better":[145],"effects":[147],"compared":[148],"swin":[152],"series.":[154],"also":[156],"involve":[157],"test":[158],"time":[159],"augmentation":[160],"improve":[162],"interpretability.":[165],"Finally,":[166],"no":[167],"model":[168],"provides":[169],"one-size-fits-all":[171],"solution":[172],"feasible":[175],"interpretable":[176],"model.":[177],"suitable":[181,191],"in-distribution":[183],"samples,":[184],"while":[185],"ViT":[186],"its":[188],"variants":[189],"interpreting":[193],"out-of-distribution":[194],"samples.":[195]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-02-12T00:00:00"}