{"id":"https://openalex.org/W7151871996","doi":"https://doi.org/10.48550/arxiv.2604.05409","title":"CRISP: Rank-Guided Iterative Squeezing for Robust Medical Image Segmentation under Domain Shift","display_name":"CRISP: Rank-Guided Iterative Squeezing for Robust Medical Image Segmentation under Domain Shift","publication_year":2026,"publication_date":"2026-04-07","ids":{"openalex":"https://openalex.org/W7151871996","doi":"https://doi.org/10.48550/arxiv.2604.05409"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.05409","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.05409","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.2604.05409","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133213773","display_name":"Yizhou Fang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fang, Yizhou","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025516568","display_name":"Pujin Cheng","orcid":"https://orcid.org/0009-0000-4844-5146"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cheng, Pujin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124753272","display_name":"Yixiang Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yixiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133191806","display_name":"Xiaoying Tang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tang, Xiaoying","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5010191987","display_name":"Longxi Zhou","orcid":"https://orcid.org/0000-0003-0116-9361"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Longxi","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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.5205000042915344,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.5205000042915344,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.11670000106096268,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.10760000348091125,"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/prior-probability","display_name":"Prior probability","score":0.6729999780654907},{"id":"https://openalex.org/keywords/voxel","display_name":"Voxel","score":0.6481999754905701},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.6044999957084656},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5720000267028809},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5149999856948853},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4934999942779541},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4756999909877777},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4756999909877777},{"id":"https://openalex.org/keywords/false-positive-paradox","display_name":"False positive paradox","score":0.45100000500679016},{"id":"https://openalex.org/keywords/translation","display_name":"Translation (biology)","score":0.4156000018119812}],"concepts":[{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.6729999780654907},{"id":"https://openalex.org/C54170458","wikidata":"https://www.wikidata.org/wiki/Q663554","display_name":"Voxel","level":2,"score":0.6481999754905701},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6057999730110168},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.6044999957084656},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5979999899864197},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5720000267028809},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5149999856948853},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4934999942779541},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4756999909877777},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4756999909877777},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.45100000500679016},{"id":"https://openalex.org/C149364088","wikidata":"https://www.wikidata.org/wiki/Q185917","display_name":"Translation (biology)","level":4,"score":0.4156000018119812},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.40790000557899475},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3840000033378601},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.3788999915122986},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.36570000648498535},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.3562000095844269},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.34439998865127563},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.33230000734329224},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.3086000084877014},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.30230000615119934},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.2939000129699707},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.2904999852180481},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.28940001130104065},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.2858999967575073},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.2849000096321106},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.2685000002384186},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.26759999990463257},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2630000114440918},{"id":"https://openalex.org/C147061848","wikidata":"https://www.wikidata.org/wiki/Q1934245","display_name":"Stable distribution","level":2,"score":0.25999999046325684},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.25839999318122864},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2549000084400177},{"id":"https://openalex.org/C2776029896","wikidata":"https://www.wikidata.org/wiki/Q3935810","display_name":"Relaxation (psychology)","level":2,"score":0.2517000138759613}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.05409","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.05409","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.2604.05409","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.05409","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":[{"display_name":"Peace, Justice and strong institutions","score":0.7744276523590088,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Distribution":[0],"shift":[1,134],"in":[2,26,53],"medical":[3,14],"imaging":[4],"remains":[5,93],"a":[6,105],"central":[7],"bottleneck":[8],"for":[9,35,90],"the":[10,54,84,116,157,202],"clinical":[11],"translation":[12],"of":[13,78,87,228],"AI.":[15],"Failure":[16],"to":[17,22,119,156,201,230],"address":[18,67],"it":[19],"can":[20],"lead":[21],"severe":[23],"performance":[24],"degradation":[25],"unseen":[27],"environments":[28],"and":[29,56,107,159,178,182,197,211,237,245],"exacerbate":[30],"health":[31],"inequities.":[32],"Existing":[33],"methods":[34,223],"domain":[36],"adaptation":[37],"are":[38,63],"inherently":[39],"limited":[40],"by":[41,99],"exhausting":[42],"predefined":[43],"possibilities":[44],"through":[45],"simulated":[46],"shifts":[47,62],"or":[48],"pseudo-supervision.":[49],"Such":[50],"strategies":[51],"struggle":[52],"open-ended":[55],"unpredictable":[57],"real":[58],"world,":[59],"where":[60,139],"distribution":[61,96,133],"effectively":[64],"infinite.":[65],"To":[66],"this":[68,100],"challenge,":[69],"we":[70,102,174],"introduce":[71],"an":[72,191],"empirical":[73],"law":[74],"called":[75],"``Rank":[76],"Stability":[77],"Positive":[79],"Regions'',":[80],"which":[81],"states":[82],"that":[83,148,161],"relative":[85],"rank":[86,124],"predicted":[88],"probabilities":[89,152],"positive":[91],"voxels":[92],"stable":[94,145],"under":[95,132,186],"shift.":[97],"Guided":[98],"principle,":[101],"propose":[103],"CRISP,":[104],"parameter-free":[106],"model-agnostic":[108],"framework":[109,118],"requiring":[110],"no":[111],"target-domain":[112],"information.":[113],"CRISP":[114,128],"is":[115],"first":[117],"make":[120],"segmentation":[121,215],"based":[122],"on":[123,171,207],"rather":[125],"than":[126],"probabilities.":[127],"simulates":[129],"model":[130],"behavior":[131],"via":[135],"latent":[136],"feature":[137],"perturbation,":[138],"voxel":[140],"probability":[141],"rankings":[142],"exhibit":[143],"two":[144],"patterns:":[146],"regions":[147],"consistently":[149],"retain":[150],"high":[151],"(destined":[153],"positives":[154],"according":[155],"principle)":[158],"those":[160],"remain":[162],"low-probability":[163],"(can":[164],"be":[165],"safely":[166],"classified":[167],"as":[168],"negatives).":[169],"Based":[170],"these":[172],"patterns,":[173],"construct":[175],"high-precision":[176],"(HP)":[177],"high-recall":[179],"(HR)":[180],"priors":[181],"recursively":[183],"refine":[184],"them":[185],"perturbation.":[187],"We":[188],"then":[189],"design":[190],"iterative":[192],"training":[193],"framework,":[194],"making":[195],"HP":[196],"HR":[198],"progressively":[199],"``squeeze''":[200],"final":[203],"segmentation.":[204],"Extensive":[205],"evaluations":[206],"multi-center":[208],"cardiac":[209],"MRI":[210],"CT-based":[212],"lung":[213],"vessel":[214],"demonstrate":[216],"CRISP's":[217],"superior":[218],"robustness,":[219],"significantly":[220],"outperforming":[221],"state-of-the-art":[222],"with":[224],"striking":[225],"HD95":[226],"reductions":[227],"up":[229],"0.14":[231],"(7.0\\%":[232],"improvement),":[233,236],"1.90":[234],"(13.1\\%":[235],"8.39":[238],"(38.9\\%":[239],"improvement)":[240],"pixels":[241],"across":[242],"multi-center,":[243],"demographic,":[244],"modality":[246],"shifts,":[247],"respectively.":[248]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-09T00:00:00"}
