{"id":"https://openalex.org/W7167014015","doi":"https://doi.org/10.48550/arxiv.2607.00499","title":"Prior-Anchored Debiasing for Long-Tailed Multi-Organ Pathology Report Generation","display_name":"Prior-Anchored Debiasing for Long-Tailed Multi-Organ Pathology Report Generation","publication_year":2026,"publication_date":"2026-07-01","ids":{"openalex":"https://openalex.org/W7167014015","doi":"https://doi.org/10.48550/arxiv.2607.00499"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.00499","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.00499","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2607.00499","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139922808","display_name":"Feng Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Feng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139923519","display_name":"Jie Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Jie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139892655","display_name":"Yubo Pang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pang, Yubo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139870453","display_name":"Peilin Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Peilin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101206691","display_name":"Xinheng Lyu","orcid":"https://orcid.org/0009-0009-5342-5495"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lyu, Xinheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139865049","display_name":"Shiqi Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Shiqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139944334","display_name":"Howard Leung","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Leung, Howard","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139892412","display_name":"Ping Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Ping","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/T10862","display_name":"AI in cancer detection","score":0.5231999754905701,"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":0.5231999754905701,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.19189999997615814,"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"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.04490000009536743,"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/debiasing","display_name":"Debiasing","score":0.8776000142097473},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.6988999843597412},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5461999773979187},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.5440999865531921},{"id":"https://openalex.org/keywords/digital-pathology","display_name":"Digital pathology","score":0.4943000078201294},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.49000000953674316},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.4668000042438507},{"id":"https://openalex.org/keywords/narrative","display_name":"Narrative","score":0.3497999906539917}],"concepts":[{"id":"https://openalex.org/C2779458634","wikidata":"https://www.wikidata.org/wiki/Q24963715","display_name":"Debiasing","level":2,"score":0.8776000142097473},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7394999861717224},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.6988999843597412},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5461999773979187},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.5440999865531921},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5270000100135803},{"id":"https://openalex.org/C2777522853","wikidata":"https://www.wikidata.org/wiki/Q5276128","display_name":"Digital pathology","level":2,"score":0.4943000078201294},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.49000000953674316},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.4668000042438507},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4636000096797943},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4219000041484833},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.42100000381469727},{"id":"https://openalex.org/C199033989","wikidata":"https://www.wikidata.org/wiki/Q1318295","display_name":"Narrative","level":2,"score":0.3497999906539917},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.33880001306533813},{"id":"https://openalex.org/C99821215","wikidata":"https://www.wikidata.org/wiki/Q1136583","display_name":"Swap (finance)","level":2,"score":0.33719998598098755},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.33340001106262207},{"id":"https://openalex.org/C2779974597","wikidata":"https://www.wikidata.org/wiki/Q28448986","display_name":"Clinical Practice","level":2,"score":0.2937000095844269},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.28600001335144043},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.28290000557899475},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.2815000116825104},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.28119999170303345},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.25870001316070557},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.25060001015663147}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.00499","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.00499","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2607.00499","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.00499","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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/10","score":0.7143182754516602,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Automated":[0],"pathology":[1,95,158],"report":[2,96,171],"generation":[3,172],"from":[4,149],"Whole":[5],"Slide":[6],"Images":[7],"(WSIs)":[8],"has":[9],"attracted":[10],"increasing":[11],"attention":[12],"in":[13,30],"digital":[14],"pathology.":[15],"However,":[16],"existing":[17],"methods":[18],"are":[19],"predominantly":[20],"developed":[21],"under":[22],"single-organ":[23],"settings,":[24],"overlooking":[25],"the":[26,55,70,107,146],"multi-organ":[27,157],"scenarios":[28],"encountered":[29],"clinical":[31],"practice,":[32],"where":[33,54,69],"organ":[34,179],"types":[35],"typically":[36],"follow":[37],"a":[38,91,101,128,156],"long-tailed":[39],"distribution.":[40],"To":[41,84],"address":[42],"this":[43],"gap,":[44],"we":[45,89],"identify":[46],"two":[47,87],"critical":[48],"biases:":[49],"(1)":[50],"visual":[51,120],"representation":[52],"bias,":[53,68],"encoder":[56],"favors":[57],"head-class":[58,74,150],"patterns":[59],"over":[60],"tail-class":[61,82],"discriminative":[62],"features,":[63],"and":[64,139,168,177],"(2)":[65],"textual":[66,142],"decoding":[67],"decoder":[71,147],"overfits":[72],"to":[73,115,144,182],"narrative":[75,151],"patterns,":[76],"yielding":[77],"diagnostically":[78,118],"unreliable":[79],"outputs":[80],"for":[81],"organs.":[83],"mitigate":[85],"these":[86],"biases,":[88],"propose":[90],"novel":[92],"Prior-anchored":[93],"multi-Organ":[94],"Generation":[97],"framework":[98],"(PriOrGen).":[99],"Specifically,":[100],"Visual-Prototype":[102],"Anchored":[103,130],"Bottleneck":[104],"module":[105,132],"leverages":[106],"information":[108,121],"bottleneck":[109],"principle":[110],"with":[111],"learnable":[112],"anchor":[113],"representations":[114],"selectively":[116],"retain":[117],"relevant":[119],"while":[122],"filtering":[123],"out":[124],"head-biased":[125],"redundancy.":[126],"Secondly,":[127],"Meta-Report":[129],"Bank":[131],"constructs":[133],"an":[134],"organ-specific":[135],"meta-report":[136],"anchored":[137],"bank":[138],"retrieves":[140],"organ-faithful":[141],"priors":[143],"steer":[145],"away":[148],"patterns.":[152],"Extensive":[153],"experiments":[154],"on":[155],"dataset":[159],"demonstrate":[160],"that":[161],"our":[162],"method":[163],"effectively":[164],"mitigates":[165],"long-tail":[166],"biases":[167],"achieves":[169],"superior":[170],"performance":[173],"across":[174],"both":[175],"head":[176],"tail":[178],"categories":[180],"compared":[181],"state-of-the-art":[183],"methods.":[184]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-03T00:00:00"}
