{"id":"https://openalex.org/W7128811168","doi":"https://doi.org/10.48550/arxiv.2602.11467","title":"PRISM: A 3D Probabilistic Neural Representation for Interpretable Shape Modeling","display_name":"PRISM: A 3D Probabilistic Neural Representation for Interpretable Shape Modeling","publication_year":2026,"publication_date":"2026-02-12","ids":{"openalex":"https://openalex.org/W7128811168","doi":"https://doi.org/10.48550/arxiv.2602.11467"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.11467","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","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":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5007602736","display_name":"Yining Jiao","orcid":"https://orcid.org/0000-0003-0311-2377"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Jiao, Yining","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5116292958","display_name":"Sreekalyani Bhamidi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bhamidi, Sreekalyani","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125932177","display_name":"Carlton Jude Zdanski","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zdanski, Carlton Jude","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Kimbell, Julia S","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kimbell, Julia S","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125943972","display_name":"Andrew Prince","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Prince, Andrew","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Worden, Cameron P","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Worden, Cameron P","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065891377","display_name":"Samuel N. Kirse","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kirse, Samuel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044557194","display_name":"Christopher Rutter","orcid":"https://orcid.org/0000-0001-8406-8130"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rutter, Christopher","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125893301","display_name":"Benjamin H Shields","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shields, Benjamin H","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005858819","display_name":"Jisan Mahmud","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mahmud, Jisan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5108610850","display_name":"Marc Niethammer","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Niethammer, Marc","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":11,"corresponding_author_ids":["https://openalex.org/A5007602736"],"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/T12417","display_name":"Morphological variations and asymmetry","score":0.4975000023841858,"subfield":{"id":"https://openalex.org/subfields/2608","display_name":"Geometry and Topology"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12417","display_name":"Morphological variations and asymmetry","score":0.4975000023841858,"subfield":{"id":"https://openalex.org/subfields/2608","display_name":"Geometry and Topology"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.07169999927282333,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.06719999760389328,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.654699981212616},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5996000170707703},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5964000225067139},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.48899999260902405},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4717999994754791},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.46790000796318054},{"id":"https://openalex.org/keywords/uncertainty-quantification","display_name":"Uncertainty quantification","score":0.4634000062942505},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4309999942779541},{"id":"https://openalex.org/keywords/covariate","display_name":"Covariate","score":0.3824999928474426}],"concepts":[{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.654699981212616},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6230999827384949},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6115000247955322},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5996000170707703},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5964000225067139},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.48899999260902405},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4717999994754791},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.46790000796318054},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.4634000062942505},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4334000051021576},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4309999942779541},{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.3824999928474426},{"id":"https://openalex.org/C43555835","wikidata":"https://www.wikidata.org/wiki/Q2300258","display_name":"Conditional probability distribution","level":2,"score":0.36149999499320984},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.3564000129699707},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.3416000008583069},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3199000060558319},{"id":"https://openalex.org/C112604564","wikidata":"https://www.wikidata.org/wiki/Q7489226","display_name":"Shape analysis (program analysis)","level":3,"score":0.31540000438690186},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.29269999265670776},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.2858999967575073},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.28299999237060547},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.27300000190734863},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.2694000005722046},{"id":"https://openalex.org/C2982736386","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Statistical learning","level":2,"score":0.2685000002384186},{"id":"https://openalex.org/C149364088","wikidata":"https://www.wikidata.org/wiki/Q185917","display_name":"Translation (biology)","level":4,"score":0.26499998569488525},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2605000138282776},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.259799987077713},{"id":"https://openalex.org/C118317068","wikidata":"https://www.wikidata.org/wiki/Q2100760","display_name":"Point distribution model","level":2,"score":0.2549999952316284},{"id":"https://openalex.org/C129641003","wikidata":"https://www.wikidata.org/wiki/Q267189","display_name":"Active shape model","level":3,"score":0.250900000333786}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.11467","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.11467","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.11467","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.11467","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Understanding":[0],"how":[1],"anatomical":[2],"shapes":[3,56],"evolve":[4],"in":[5,18],"response":[6],"to":[7],"developmental":[8],"covariates":[9],"and":[10,68,101,119],"quantifying":[11],"their":[12],"spatially":[13,31,60],"varying":[14],"uncertainties":[15],"is":[16,78],"critical":[17],"healthcare":[19],"research.":[20],"Existing":[21],"approaches":[22],"typically":[23],"rely":[24],"on":[25,97],"global":[26],"time-warping":[27],"formulations":[28],"that":[29,40,84],"ignore":[30],"heterogeneous":[32],"dynamics.":[33],"We":[34],"introduce":[35],"PRISM,":[36],"a":[37,79,113],"novel":[38],"framework":[39],"bridges":[41],"implicit":[42],"neural":[43],"representations":[44],"with":[45],"uncertainty-aware":[46],"statistical":[47],"shape":[48],"analysis.":[49],"PRISM":[50],"models":[51],"the":[52,65],"conditional":[53],"distribution":[54],"of":[55,63],"given":[57],"covariates,":[58],"providing":[59,117],"continuous":[61],"estimates":[62],"both":[64],"population":[66],"mean":[67],"covariate-dependent":[69],"uncertainty":[70,91,122],"at":[71],"arbitrary":[72],"locations.":[73],"A":[74],"key":[75],"theoretical":[76],"contribution":[77],"closed-form":[80],"Fisher":[81],"Information":[82],"metric":[83],"enables":[85],"efficient,":[86],"analytically":[87],"tractable":[88],"local":[89],"temporal":[90],"quantification":[92],"via":[93],"automatic":[94],"differentiation.":[95],"Experiments":[96],"three":[98],"synthetic":[99],"datasets":[100],"one":[102],"clinical":[103],"dataset":[104],"demonstrate":[105],"PRISM's":[106],"strong":[107],"performance":[108],"across":[109],"diverse":[110],"tasks":[111],"within":[112],"unified":[114],"framework,":[115],"while":[116],"interpretable":[118],"clinically":[120],"meaningful":[121],"estimates.":[123]},"counts_by_year":[],"updated_date":"2026-05-05T08:41:31.759640","created_date":"2026-02-14T00:00:00"}