{"id":"https://openalex.org/W7152408104","doi":"https://doi.org/10.48550/arxiv.2604.06390","title":"MorphDistill: Distilling Unified Morphological Knowledge from Pathology Foundation Models for Colorectal Cancer Survival Prediction","display_name":"MorphDistill: Distilling Unified Morphological Knowledge from Pathology Foundation Models for Colorectal Cancer Survival Prediction","publication_year":2026,"publication_date":"2026-04-07","ids":{"openalex":"https://openalex.org/W7152408104","doi":"https://doi.org/10.48550/arxiv.2604.06390"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.06390","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.06390","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.06390","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133285312","display_name":"Hikmat Khan","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Khan, Hikmat","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133268613","display_name":"Usama Sajjad","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sajjad, Usama","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133298210","display_name":"Metin N. Gurcan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gurcan, Metin N.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133289301","display_name":"Anil Parwani","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Parwani, Anil","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133240535","display_name":"Wendy L. Frankel","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Frankel, Wendy L.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133251637","display_name":"Dr. Wei Chen","orcid":"https://orcid.org/0000-0003-3713-6633"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Wei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133283880","display_name":"Muhammad Khalid Khan Niazi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Niazi, Muhammad Khalid Khan","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5133285312"],"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":0.5907999873161316,"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":0.5907999873161316,"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/T10552","display_name":"Colorectal Cancer Screening and Detection","score":0.25,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.020400000736117363,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/colorectal-cancer","display_name":"Colorectal cancer","score":0.4607999920845032},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4399000108242035},{"id":"https://openalex.org/keywords/disease","display_name":"Disease","score":0.40130001306533813},{"id":"https://openalex.org/keywords/cohort","display_name":"Cohort","score":0.4004000127315521},{"id":"https://openalex.org/keywords/foundation","display_name":"Foundation (evidence)","score":0.3804999887943268},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.3531000018119812},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.3379000127315521},{"id":"https://openalex.org/keywords/survival-analysis","display_name":"Survival analysis","score":0.326200008392334}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.625},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5403000116348267},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5356000065803528},{"id":"https://openalex.org/C526805850","wikidata":"https://www.wikidata.org/wiki/Q188874","display_name":"Colorectal cancer","level":3,"score":0.4607999920845032},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4399000108242035},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.40799999237060547},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.40130001306533813},{"id":"https://openalex.org/C72563966","wikidata":"https://www.wikidata.org/wiki/Q1303415","display_name":"Cohort","level":2,"score":0.4004000127315521},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.3804999887943268},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.3531000018119812},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.3379000127315521},{"id":"https://openalex.org/C10515644","wikidata":"https://www.wikidata.org/wiki/Q543310","display_name":"Survival analysis","level":2,"score":0.326200008392334},{"id":"https://openalex.org/C50382708","wikidata":"https://www.wikidata.org/wiki/Q223218","display_name":"Proportional hazards model","level":2,"score":0.3050999939441681},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.3021000027656555},{"id":"https://openalex.org/C207103383","wikidata":"https://www.wikidata.org/wiki/Q3930246","display_name":"Hazard ratio","level":3,"score":0.3021000027656555},{"id":"https://openalex.org/C146357865","wikidata":"https://www.wikidata.org/wiki/Q1123245","display_name":"Stage (stratigraphy)","level":2,"score":0.3009999990463257},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2994000017642975},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.29760000109672546},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.29100000858306885},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.2904999852180481},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.29010000824928284},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.2727999985218048},{"id":"https://openalex.org/C143998085","wikidata":"https://www.wikidata.org/wiki/Q162555","display_name":"Oncology","level":1,"score":0.26829999685287476},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2596000134944916},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.2556000053882599}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.06390","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.06390","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":"doi:10.48550/arxiv.2604.06390","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.06390","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":"article"},"sustainable_development_goals":[{"score":0.6573356986045837,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Background:":[0],"Colorectal":[1],"cancer":[2],"(CRC)":[3],"remains":[4],"a":[5,37,50,57,142,147,166,194],"leading":[6],"cause":[7],"of":[8,124,144,150,168],"cancer-related":[9],"mortality":[10],"worldwide.":[11],"Accurate":[12],"survival":[13],"prediction":[14],"is":[15,60,223],"essential":[16],"for":[17,30,203,211],"treatment":[18],"stratification,":[19],"yet":[20],"existing":[21],"pathology":[22,46],"foundation":[23,47,81,191],"models":[24,48,82,192],"often":[25],"overlook":[26],"organ-specific":[27],"features":[28,94],"critical":[29],"CRC":[31],"prognostication.":[32],"Methods:":[33],"We":[34],"propose":[35],"MorphDistill,":[36],"two-stage":[38],"framework":[39],"that":[40],"distills":[41],"complementary":[42],"knowledge":[43,188],"from":[44,79,95,189],"multiple":[45,103,190],"into":[49,193],"compact":[51],"CRC-specific":[52],"encoder.":[53,196],"In":[54,87],"Stage":[55,88],"I,":[56],"student":[58],"encoder":[59,91],"trained":[61],"using":[62],"dimension-agnostic":[63],"multi-teacher":[64],"relational":[65],"distillation":[66],"with":[67,209],"supervised":[68],"contrastive":[69],"regularization":[70],"on":[71],"large-scale":[72],"colorectal":[73],"datasets.":[74],"This":[75,197],"preserves":[76],"inter-sample":[77],"relationships":[78],"ten":[80],"without":[83],"explicit":[84],"feature":[85],"alignment.":[86],"II,":[89],"the":[90,112,134],"extracts":[92],"patch-level":[93],"whole-slide":[96],"images,":[97],"which":[98],"are":[99],"aggregated":[100],"via":[101],"attention-based":[102],"instance":[104],"learning":[105,185],"to":[106],"predict":[107],"five-year":[108],"survival.":[109],"Results:":[110],"On":[111,158],"Alliance/CALGB":[113],"89803":[114],"cohort":[115,162],"(n=424,":[116],"stage":[117],"III":[118],"CRC),":[119],"MorphDistill":[120,181],"achieves":[121,165],"an":[122,128,159,200],"AUC":[123],"0.68":[125],"(SD":[126],"0.08),":[127],"approximately":[129],"8%":[130],"relative":[131],"improvement":[132],"over":[133],"strongest":[135],"baseline":[136],"(AUC":[137],"0.63).":[138],"It":[139],"also":[140],"attains":[141],"C-index":[143,167],"0.661":[145],"and":[146,175,220],"hazard":[148],"ratio":[149],"2.52":[151],"(95%":[152],"CI:":[153],"1.73-3.65),":[154],"outperforming":[155],"all":[156],"baselines.":[157],"external":[160],"TCGA":[161],"(n=562),":[163],"it":[164],"0.628,":[169],"demonstrating":[170],"strong":[171],"generalization":[172],"across":[173,177,217],"datasets":[174],"robustness":[176],"clinical":[178],"subgroups.":[179],"Conclusion:":[180],"enables":[182],"task-specific":[183],"representation":[184],"by":[186],"integrating":[187],"unified":[195],"approach":[198],"provides":[199],"efficient":[201],"strategy":[202],"prognostic":[204],"modeling":[205],"in":[206],"computational":[207],"pathology,":[208],"potential":[210],"broader":[212],"oncology":[213],"applications.":[214],"Further":[215],"validation":[216],"additional":[218],"cohorts":[219],"disease":[221],"stages":[222],"warranted.":[224]},"counts_by_year":[],"updated_date":"2026-04-10T06:07:51.998497","created_date":"2026-04-10T00:00:00"}
