{"id":"https://openalex.org/W7147207900","doi":"https://doi.org/10.48550/arxiv.2603.29449","title":"NeoNet: An End-to-End 3D MRI-Based Deep Learning Framework for Non-Invasive Prediction of Perineural Invasion via Generation-Driven Classification","display_name":"NeoNet: An End-to-End 3D MRI-Based Deep Learning Framework for Non-Invasive Prediction of Perineural Invasion via Generation-Driven Classification","publication_year":2026,"publication_date":"2026-03-31","ids":{"openalex":"https://openalex.org/W7147207900","doi":"https://doi.org/10.48550/arxiv.2603.29449"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.29449","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.29449","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"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.29449","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5111350841","display_name":"Youngung Han","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Han, Youngung","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068637562","display_name":"Minkyung Cha","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cha, Minkyung","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132635493","display_name":"Kyeonghun Kim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Kyeonghun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132573308","display_name":"Induk Um","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Um, Induk","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132672015","display_name":"Myeongbin Sho","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sho, Myeongbin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039322291","display_name":"Joo Young Bae","orcid":"https://orcid.org/0009-0008-6996-2714"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bae, Joo Young","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132543031","display_name":"Jaewon Jung","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jung, Jaewon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006491113","display_name":"Jung Hyeok Park","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Park, Jung Hyeok","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132718178","display_name":"Seojun Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Seojun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000717294","display_name":"Nam-Joon Kim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Nam-Joon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009904795","display_name":"Woo Kyoung Jeong","orcid":"https://orcid.org/0000-0002-0676-2116"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jeong, Woo Kyoung","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132585912","display_name":"Won Jae Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Won Jae","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059952027","display_name":"Pa Hong","orcid":"https://orcid.org/0000-0001-5495-5230"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hong, Pa","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028602586","display_name":"Ken Ying-Kai Liao","orcid":"https://orcid.org/0000-0001-7815-8199"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liao, Ken Ying-Kai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5102861073","display_name":"Hyuk-Jae Lee","orcid":"https://orcid.org/0000-0001-8895-9117"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Hyuk-Jae","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/T11364","display_name":"Cholangiocarcinoma and Gallbladder Cancer Studies","score":0.17489999532699585,"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"}},"topics":[{"id":"https://openalex.org/T11364","display_name":"Cholangiocarcinoma and Gallbladder Cancer Studies","score":0.17489999532699585,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.13650000095367432,"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/T10231","display_name":"Pancreatic and Hepatic Oncology Research","score":0.10239999741315842,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7282000184059143},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3928999900817871},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.3652999997138977},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.3183000087738037},{"id":"https://openalex.org/keywords/perineural-invasion","display_name":"Perineural invasion","score":0.31290000677108765},{"id":"https://openalex.org/keywords/3d-model","display_name":"3d model","score":0.29820001125335693}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7457000017166138},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7282000184059143},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6463000178337097},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3928999900817871},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38769999146461487},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.3652999997138977},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.3183000087738037},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.31779998540878296},{"id":"https://openalex.org/C2777154038","wikidata":"https://www.wikidata.org/wiki/Q7168585","display_name":"Perineural invasion","level":3,"score":0.31290000677108765},{"id":"https://openalex.org/C3019007443","wikidata":"https://www.wikidata.org/wiki/Q568742","display_name":"3d model","level":2,"score":0.29820001125335693},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.29429998993873596},{"id":"https://openalex.org/C19609008","wikidata":"https://www.wikidata.org/wiki/Q2138203","display_name":"Region of interest","level":2,"score":0.2883000075817108},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.28279998898506165},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.2718999981880188},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.26010000705718994}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.29449","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.29449","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.2603.29449","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.29449","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":[{"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being","score":0.457052081823349}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Minimizing":[0],"invasive":[1],"diagnostic":[2],"procedures":[3],"to":[4,45,112,121,154],"reduce":[5],"the":[6,38,46,119,128,136,142,175],"risk":[7],"of":[8,24,34,48,163,182],"patient":[9],"injury":[10],"and":[11,50,125,146,159,173],"infection":[12],"is":[13],"a":[14,28,92,100,122,179],"central":[15],"goal":[16],"in":[17,75],"medical":[18],"imaging.":[19],"And":[20],"yet,":[21],"noninvasive":[22],"diagnosis":[23],"perineural":[25],"invasion":[26],"(PNI),":[27],"critical":[29],"prognostic":[30],"factor":[31],"involving":[32],"infiltration":[33],"tumor":[35],"cells":[36],"along":[37],"surrounding":[39],"nerve,":[40],"still":[41],"remains":[42],"challenging,":[43],"due":[44],"lack":[47],"clear":[49],"consistent":[51],"imaging":[52],"criteria":[53,54],"for":[55,72],"identifying":[56],"PNI.":[57,164],"To":[58],"address":[59],"this":[60],"challenge,":[61],"we":[62,134],"present":[63],"NeoNet,":[64],"an":[65],"integrated":[66],"end-to-end":[67],"3D":[68,101,148,171],"deep":[69],"learning":[70],"framework":[71],"PNI":[73],"prediction":[74,130],"cholangiocarcinoma":[76],"that":[77],"does":[78],"not":[79],"rely":[80],"on":[81,109],"predefined":[82],"image":[83,115],"features.":[84],"NeoNet":[85,168],"integrates":[86],"three":[87],"modules:":[88],"(1)":[89],"NeoSeg,":[90],"utilizing":[91],"Tumor-Localized":[93],"ROI":[94],"Crop":[95],"(TLCR)":[96],"algorithm;":[97],"(2)":[98],"NeoGen,":[99],"Latent":[102],"Diffusion":[103],"Model":[104],"(LDM)":[105],"with":[106,178],"ControlNet,":[107],"conditioned":[108],"anatomical":[110],"masks":[111],"generate":[113],"synthetic":[114],"patches,":[116],"specifically":[117],"balancing":[118],"dataset":[120],"1:1":[123],"ratio;":[124],"(3)":[126],"NeoCls,":[127,133],"final":[129],"module.":[131],"For":[132],"developed":[135],"PNI-Attention":[137],"Network":[138],"(PattenNet),":[139],"which":[140],"uses":[141],"frozen":[143],"LDM":[144],"encoder":[145],"specialized":[147],"Dual":[149],"Attention":[150],"Blocks":[151],"(DAB)":[152],"designed":[153],"detect":[155],"subtle":[156],"intensity":[157],"variations":[158],"spatial":[160],"patterns":[161],"indicative":[162],"In":[165],"5-fold":[166],"cross-validation,":[167],"outperformed":[169],"baseline":[170],"models":[172],"achieved":[174],"highest":[176],"performance":[177],"maximum":[180],"AUC":[181],"0.7903.":[183]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-02T00:00:00"}
