{"id":"https://openalex.org/W7106143528","doi":"https://doi.org/10.48550/arxiv.2511.14021","title":"MRI Plane Orientation Detection using a Context-Aware 2.5D Model","display_name":"MRI Plane Orientation Detection using a Context-Aware 2.5D Model","publication_year":2025,"publication_date":"2025-11-18","ids":{"openalex":"https://openalex.org/W7106143528","doi":"https://doi.org/10.48550/arxiv.2511.14021"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2511.14021","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2511.14021","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.2511.14021","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Kim, SangHyuk","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Kim, SangHyuk","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Haehn, Daniel","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Haehn, Daniel","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Rampersad, Sumientra","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rampersad, Sumientra","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.43309998512268066,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.43309998512268066,"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/T14510","display_name":"Medical Imaging and Analysis","score":0.07609999924898148,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.06970000267028809,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/orientation","display_name":"Orientation (vector space)","score":0.6699000000953674},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6101999878883362},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.5950999855995178},{"id":"https://openalex.org/keywords/metadata","display_name":"Metadata","score":0.5756999850273132},{"id":"https://openalex.org/keywords/ambiguity","display_name":"Ambiguity","score":0.5659999847412109},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4142000079154968},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.37950000166893005}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7067000269889832},{"id":"https://openalex.org/C16345878","wikidata":"https://www.wikidata.org/wiki/Q107472979","display_name":"Orientation (vector space)","level":2,"score":0.6699000000953674},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6614999771118164},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6101999878883362},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.5950999855995178},{"id":"https://openalex.org/C93518851","wikidata":"https://www.wikidata.org/wiki/Q180160","display_name":"Metadata","level":2,"score":0.5756999850273132},{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.5659999847412109},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5591999888420105},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4142000079154968},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.37950000166893005},{"id":"https://openalex.org/C17825722","wikidata":"https://www.wikidata.org/wiki/Q17285","display_name":"Plane (geometry)","level":2,"score":0.3402999937534332},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.33059999346733093},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.31450000405311584},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.2955000102519989},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2784999907016754},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.27320000529289246},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2648000121116638},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.25780001282691956}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2511.14021","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2511.14021","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.2511.14021","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2511.14021","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Humans":[0],"can":[1,26],"easily":[2],"identify":[3],"anatomical":[4],"planes":[5],"(axial,":[6],"coronal,":[7],"and":[8,36,69,84,163],"sagittal)":[9],"on":[10,79,135],"a":[11,45,55,122],"2D":[12,86,90],"MRI":[13],"slice,":[14],"but":[15],"automated":[16],"systems":[17],"struggle":[18],"with":[19,142],"this":[20,100],"task.":[21,126],"Missing":[22],"plane":[23,50,155],"orientation":[24,51,156],"metadata":[25,120],"complicate":[27],"analysis,":[28],"increase":[29],"domain":[30],"shift":[31],"when":[32],"merging":[33],"heterogeneous":[34],"datasets,":[35],"reduce":[37],"accuracy":[38,139],"of":[39,110,117],"diagnostic":[40],"classifiers.":[41],"This":[42],"study":[43],"develops":[44],"classifier":[46],"that":[47,59],"accurately":[48],"generates":[49],"metadata.":[52],"We":[53,74,113,152],"adopt":[54],"2.5D":[56,77,97,111],"context-aware":[57],"model":[58,78,92,145,157],"leverages":[60],"multi-slice":[61],"information":[62],"to":[63,101,146],"avoid":[64],"ambiguity":[65],"from":[66,140],"isolated":[67],"slices":[68],"enable":[70],"robust":[71],"feature":[72],"learning.":[73],"train":[75],"the":[76,108,115],"both":[80],"3D":[81],"slice":[82],"sequences":[83],"static":[85],"images.":[87],"While":[88],"our":[89,96,118,154],"reference":[91],"achieves":[93],"98.74%":[94],"accuracy,":[95],"method":[98],"raises":[99],"99.49%,":[102],"reducing":[103,148],"errors":[104],"by":[105,150],"60%,":[106],"highlighting":[107],"importance":[109],"context.":[112],"validate":[114],"utility":[116],"generated":[119],"in":[121],"brain":[123],"tumor":[124],"detection":[125],"A":[127],"gated":[128],"strategy":[129],"selectively":[130],"uses":[131],"metadata-enhanced":[132],"predictions":[133],"based":[134],"uncertainty":[136],"scores,":[137],"boosting":[138],"97.0%":[141],"an":[143,159],"image-only":[144],"98.0%,":[147],"misdiagnoses":[149],"33.3%.":[151],"integrate":[153],"into":[158],"interactive":[160],"web":[161],"application":[162],"provide":[164],"it":[165],"open-source.":[166]},"counts_by_year":[],"updated_date":"2025-11-20T23:17:41.989164","created_date":"2025-11-20T00:00:00"}