{"id":"https://openalex.org/W7126077191","doi":"https://doi.org/10.1109/bibm66473.2025.11356522","title":"PAD-Former: A Two-Stage PI-RADS Classification Framework with Multi-Modal Anomaly Diffusion and Cross-Attention","display_name":"PAD-Former: A Two-Stage PI-RADS Classification Framework with Multi-Modal Anomaly Diffusion and Cross-Attention","publication_year":2025,"publication_date":"2025-12-15","ids":{"openalex":"https://openalex.org/W7126077191","doi":"https://doi.org/10.1109/bibm66473.2025.11356522"},"language":null,"primary_location":{"id":"doi:10.1109/bibm66473.2025.11356522","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356522","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5124198575","display_name":"Ziyu Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I69356397","display_name":"Xi\u2019an Jiaotong-Liverpool University","ror":"https://ror.org/03zmrmn05","country_code":"CN","type":"education","lineage":["https://openalex.org/I69356397"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ziyu Zhao","raw_affiliation_strings":["School of Chips, Xi'an Jiaotong - Liverpool University,Suzhou,China"],"affiliations":[{"raw_affiliation_string":"School of Chips, Xi'an Jiaotong - Liverpool University,Suzhou,China","institution_ids":["https://openalex.org/I69356397"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108336672","display_name":"Hong Seng Gan","orcid":null},"institutions":[{"id":"https://openalex.org/I69356397","display_name":"Xi\u2019an Jiaotong-Liverpool University","ror":"https://ror.org/03zmrmn05","country_code":"CN","type":"education","lineage":["https://openalex.org/I69356397"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hong Seng Gan","raw_affiliation_strings":["School of AI and Advanced Computing, Xi'an Jiaotong - Liverpool University,Suzhou,China"],"affiliations":[{"raw_affiliation_string":"School of AI and Advanced Computing, Xi'an Jiaotong - Liverpool University,Suzhou,China","institution_ids":["https://openalex.org/I69356397"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019688563","display_name":"Yijia Cao","orcid":"https://orcid.org/0009-0005-5961-7798"},"institutions":[{"id":"https://openalex.org/I69356397","display_name":"Xi\u2019an Jiaotong-Liverpool University","ror":"https://ror.org/03zmrmn05","country_code":"CN","type":"education","lineage":["https://openalex.org/I69356397"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yijia Cao","raw_affiliation_strings":["School of AI and Advanced Computing, Xi'an Jiaotong - Liverpool University,Suzhou,China"],"affiliations":[{"raw_affiliation_string":"School of AI and Advanced Computing, Xi'an Jiaotong - Liverpool University,Suzhou,China","institution_ids":["https://openalex.org/I69356397"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124285122","display_name":"Zeynep Ayvat Ocal","orcid":null},"institutions":[{"id":"https://openalex.org/I132257509","display_name":"Izmir University","ror":"https://ror.org/013h3xr51","country_code":"TR","type":"education","lineage":["https://openalex.org/I132257509"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Zeynep Ayvat Ocal","raw_affiliation_strings":["Bakircay University,Dept. of Radiology,Izmir,Turkey"],"affiliations":[{"raw_affiliation_string":"Bakircay University,Dept. of Radiology,Izmir,Turkey","institution_ids":["https://openalex.org/I132257509"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006725795","display_name":"\u0130lker \u00d6zg\u00fcr Koska","orcid":"https://orcid.org/0000-0003-0971-3827"},"institutions":[{"id":"https://openalex.org/I4210143914","display_name":"Kent Hastanesi","ror":"https://ror.org/0599zba67","country_code":"TR","type":"healthcare","lineage":["https://openalex.org/I4210143914"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Ilker Ozgur Koska","raw_affiliation_strings":["Izmir Acibadem Kent Hospital,Dept. of Radiology,Izmir,Turkey"],"affiliations":[{"raw_affiliation_string":"Izmir Acibadem Kent Hospital,Dept. of Radiology,Izmir,Turkey","institution_ids":["https://openalex.org/I4210143914"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5124198575"],"corresponding_institution_ids":["https://openalex.org/I69356397"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.74704353,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"6162","last_page":"6169"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10124","display_name":"Prostate Cancer Diagnosis and Treatment","score":0.18019999563694,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"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/T10124","display_name":"Prostate Cancer Diagnosis and Treatment","score":0.18019999563694,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"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/T13702","display_name":"Machine Learning in Healthcare","score":0.1777999997138977,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.07800000160932541,"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/classifier","display_name":"Classifier (UML)","score":0.5870000123977661},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.5763000249862671},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5304999947547913},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.48010000586509705},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.39250001311302185},{"id":"https://openalex.org/keywords/suite","display_name":"Suite","score":0.3668000102043152},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.36250001192092896},{"id":"https://openalex.org/keywords/magnetic-resonance-imaging","display_name":"Magnetic resonance imaging","score":0.32280001044273376}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7150999903678894},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5960000157356262},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5870000123977661},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.5763000249862671},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5304999947547913},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.48010000586509705},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.39250001311302185},{"id":"https://openalex.org/C79581498","wikidata":"https://www.wikidata.org/wiki/Q1367530","display_name":"Suite","level":2,"score":0.3668000102043152},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.36250001192092896},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36010000109672546},{"id":"https://openalex.org/C143409427","wikidata":"https://www.wikidata.org/wiki/Q161238","display_name":"Magnetic resonance imaging","level":2,"score":0.32280001044273376},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.31630000472068787},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.31209999322891235},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.310699999332428},{"id":"https://openalex.org/C149550507","wikidata":"https://www.wikidata.org/wiki/Q899360","display_name":"Diffusion MRI","level":3,"score":0.3070000112056732},{"id":"https://openalex.org/C40993552","wikidata":"https://www.wikidata.org/wiki/Q514654","display_name":"Gold standard (test)","level":2,"score":0.3019999861717224},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.2985999882221222},{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.27950000762939453},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2791999876499176},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2718000113964081},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.2549999952316284}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm66473.2025.11356522","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356522","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W2744748393","https://openalex.org/W2922071185","https://openalex.org/W3094595351","https://openalex.org/W3138588354","https://openalex.org/W3172863135","https://openalex.org/W4312933868","https://openalex.org/W4366124585","https://openalex.org/W4391211384","https://openalex.org/W4401374964","https://openalex.org/W4407164754","https://openalex.org/W4412660267","https://openalex.org/W4412699270"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"diagnosis":[1],"of":[2,22,79,126,135,219,228],"clinically":[3],"significant":[4,56],"prostate":[5],"cancer":[6],"(csPCa)":[7],"is":[8,37,194],"central":[9],"to":[10,74,129,147,182],"patient":[11],"management,":[12],"with":[13],"the":[14,20,29,49,77,90,123,188,198],"clinical":[15,185,233,255],"gold":[16],"standard":[17,36],"relying":[18],"on":[19,152,197],"interpretation":[21],"multi-parametric":[23],"Magnetic":[24],"Resonance":[25],"Imaging":[26],"(mpMRI)":[27],"using":[28],"PI-RADS":[30,85,150],"v2.1":[31],"score":[32],"[1].":[33],"However,":[34],"this":[35,220],"critically":[38],"challenged":[39],"by":[40,160,177],"lesion":[41,81],"subtlety":[42],"and":[43,83,96,113,173,176,213,235,248],"substantial":[44],"interobserver":[45],"variability,":[46],"especially":[47],"within":[48],"ambiguous":[50],"PIRADS":[51],"3":[52],"category,":[53],"presenting":[54],"a":[55,67,104,114,141,162,225,241],"research":[57],"gap":[58],"for":[59,110,118,166,254],"automated":[60],"systems.":[61],"To":[62],"address":[63],"this,":[64],"we":[65],"propose":[66],"novel":[68],"twostage":[69],"deep":[70],"learning":[71],"framework":[72,193],"designed":[73],"systematically":[75],"separate":[76],"tasks":[78],"high-recall":[80,132],"localization":[82],"finegrained":[84],"classification,":[86],"while":[87],"effectively":[88],"modeling":[89],"synergistic":[91],"relationship":[92],"between":[93],"anatomical":[94],"(T2W)":[95],"functional":[97],"(ADC/DWI)":[98],"sequences.":[99],"The":[100],"first":[101],"stage":[102],"introduces":[103],"Multi-modal":[105],"Latent":[106],"Diffusion":[107],"Model":[108],"(LDM)":[109],"normative":[111],"reconstruction":[112],"Learned":[115],"Difference":[116],"Sub-network":[117],"anomaly":[119],"scoring,":[120],"which":[121],"learns":[122],"distribution":[124],"manifold":[125],"healthy":[127],"tissue":[128],"efficiently":[130],"generate":[131],"candidate":[133],"Regions":[134],"Interest":[136],"(ROIs).":[137],"Stage":[138],"2":[139],"employs":[140],"dedicated":[142],"3D":[143],"Swin":[144],"Transformer":[145],"classifier":[146,156],"perform":[148],"5-class":[149],"prediction":[151,190,236],"these":[153],"ROIs.":[154],"This":[155],"achieves":[157,245],"robust":[158,242],"performance":[159],"utilizing":[161],"Multi-Head":[163],"Cross-Attention":[164],"mechanism":[165],"dynamic,":[167],"deep-level":[168],"feature":[169],"fusion":[170,212],"across":[171,224],"T2W":[172],"ADC/DWI":[174],"modalities,":[175],"incorporating":[178],"prostatic":[179],"zonal":[180],"priors":[181],"enhance":[183],"its":[184],"contextawareness":[186],"in":[187],"final":[189],"layer.":[191],"Our":[192,238],"rigorously":[195],"validated":[196],"large-scale,":[199],"public":[200],"benchmark":[201],"PICAI":[202],"dataset.":[203],"Quantitative":[204],"ablation":[205],"studies":[206],"confirm":[207],"that":[208,244],"our":[209],"proposed":[210],"cross-modal":[211],"prior-injection":[214],"modules":[215],"are":[216],"critical":[217],"drivers":[218],"superior":[221],"performance,":[222,250],"demonstrated":[223],"comprehensive":[226],"suite":[227],"metrics":[229],"evaluating":[230],"classification":[231],"accuracy,":[232],"utility,":[234],"reliability.":[237],"work":[239],"provides":[240],"solution":[243],"state-of-the-art":[246],"accuracy":[247],"balanced":[249],"demonstrating":[251],"strong":[252],"potential":[253],"translation.":[256]},"counts_by_year":[],"updated_date":"2026-02-01T03:34:12.195049","created_date":"2026-01-30T00:00:00"}