{"id":"https://openalex.org/W6926125257","doi":"https://doi.org/10.2312/vcbm.20221193","title":"Visual Analytics to Assess Deep Learning Models for Cross-Modal Brain Tumor Segmentation","display_name":"Visual Analytics to Assess Deep Learning Models for Cross-Modal Brain Tumor Segmentation","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W6926125257","doi":"https://doi.org/10.2312/vcbm.20221193"},"language":"en","primary_location":{"id":"doi:10.2312/vcbm.20221193","is_oa":true,"landing_page_url":"https://doi.org/10.2312/vcbm.20221193","pdf_url":null,"source":{"id":"https://openalex.org/S7407052899","display_name":"Eurographics","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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"ConferencePaper"},"type":"conference-paper","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.2312/vcbm.20221193","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Magg, Caroline","orcid":null},"institutions":[{"id":"https://openalex.org/I145847075","display_name":"TU Wien","ror":"https://ror.org/04d836q62","country_code":"AT","type":"education","lineage":["https://openalex.org/I145847075"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Magg, Caroline","raw_affiliation_strings":["TU Wien"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TU Wien","institution_ids":["https://openalex.org/I145847075"]}]},{"author_position":"last","author":{"id":null,"display_name":"Raidou, Renata Georgia","orcid":"https://orcid.org/0000-0003-2468-0664"},"institutions":[{"id":"https://openalex.org/I145847075","display_name":"TU Wien","ror":"https://ror.org/04d836q62","country_code":"AT","type":"education","lineage":["https://openalex.org/I145847075"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Raidou, Renata Georgia","raw_affiliation_strings":["TU Wien, Austria"],"raw_orcid":"https://orcid.org/0000-0003-2468-0664","affiliations":[{"raw_affiliation_string":"TU Wien, Austria","institution_ids":["https://openalex.org/I145847075"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I145847075"],"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":true,"primary_topic":{"id":"https://openalex.org/T10147","display_name":"Antibiotic Resistance in Bacteria","score":0.17550000548362732,"subfield":{"id":"https://openalex.org/subfields/1313","display_name":"Molecular Medicine"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10147","display_name":"Antibiotic Resistance in Bacteria","score":0.17550000548362732,"subfield":{"id":"https://openalex.org/subfields/1313","display_name":"Molecular Medicine"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T12673","display_name":"Microbial Metabolism and Applications","score":0.04740000143647194,"subfield":{"id":"https://openalex.org/subfields/1305","display_name":"Biotechnology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10015","display_name":"Genomics and Phylogenetic Studies","score":0.016899999231100082,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.8330000042915344},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6396999955177307},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5504000186920166},{"id":"https://openalex.org/keywords/domain-knowledge","display_name":"Domain knowledge","score":0.4169999957084656},{"id":"https://openalex.org/keywords/visual-analytics","display_name":"Visual analytics","score":0.4097999930381775},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.3700999915599823},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.3538999855518341},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.35350000858306885}],"concepts":[{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.8330000042915344},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8019000291824341},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7398999929428101},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6396999955177307},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5504000186920166},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5144000053405762},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.4169999957084656},{"id":"https://openalex.org/C59732488","wikidata":"https://www.wikidata.org/wiki/Q2528440","display_name":"Visual analytics","level":3,"score":0.4097999930381775},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.3700999915599823},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.3538999855518341},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.35350000858306885},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.33180001378059387},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.3292999863624573},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.3246000111103058},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.30410000681877136},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.2847000062465668},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.2793000042438507},{"id":"https://openalex.org/C99740376","wikidata":"https://www.wikidata.org/wiki/Q17092520","display_name":"Interactive visual analysis","level":4,"score":0.26660001277923584},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.25870001316070557},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.2565999925136566},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.25450000166893005},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.25450000166893005},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.2312/vcbm.20221193","is_oa":true,"landing_page_url":"https://doi.org/10.2312/vcbm.20221193","pdf_url":null,"source":{"id":"https://openalex.org/S7407052899","display_name":"Eurographics","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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"ConferencePaper"}],"best_oa_location":{"id":"doi:10.2312/vcbm.20221193","is_oa":true,"landing_page_url":"https://doi.org/10.2312/vcbm.20221193","pdf_url":null,"source":{"id":"https://openalex.org/S7407052899","display_name":"Eurographics","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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"ConferencePaper"},"sustainable_development_goals":[{"display_name":"No poverty","score":0.6869818568229675,"id":"https://metadata.un.org/sdg/1"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"delineations":[1],"of":[2,23,75,83,102,106,122,129,138,152,156,185,192,259],"anatomically":[3],"relevant":[4],"structures":[5],"are":[6],"required":[7],"for":[8,111,256],"cancer":[9],"treatment":[10],"planning.":[11],"Despite":[12],"its":[13],"accuracy,":[14],"manual":[15],"labeling":[16],"is":[17,31,42,56,161,237],"time-consuming":[18],"and":[19,144,147,189,215,244],"tedious-hence,":[20],"the":[21,73,84,99,103,119,136,139,149,153,157,183,186,190,198,206,223,241,257],"potential":[22],"automatic":[24],"approaches,":[25,77,182],"such":[26,67],"as":[27,226],"deep":[28,38,81,107,179],"learning":[29,39,180],"models,":[30,68],"being":[32],"investigated.":[33],"A":[34],"promising":[35],"trend":[36],"in":[37,163,178,212,218,252],"tumor":[40,114,187,207],"segmentation":[41,181,261],"cross-modal":[43,112],"domain":[44],"adaptation,":[45],"where":[46],"knowledge":[47],"learned":[48],"on":[49],"one":[50,54],"source":[51],"distribution":[52],"(e.g.,":[53],"modality)":[55],"transferred":[57],"to":[58,70,132,197,239,248],"another":[59],"distribution.":[60],"Yet,":[61],"artificial":[62],"intelligence":[63],"(AI)":[64],"engineers":[65,251],"developing":[66],"need":[69],"thoroughly":[71],"assess":[72],"robustness":[74],"their":[76,245,260],"which":[78],"demands":[79],"a":[80,92],"understanding":[82],"model(s)":[85],"behavior.":[86],"In":[87],"this":[88,160],"paper,":[89],"we":[90,203],"propose":[91],"web-based":[93],"visual":[94,100],"analytics":[95],"application":[96,117],"that":[97,205],"supports":[98,118],"assessment":[101],"predictive":[104,154],"performance":[105],"learning-based":[108],"models":[109,124],"built":[110],"brain":[113],"segmentation.":[115],"Our":[116,235],"multi-level":[120],"comparison":[121],"multiple":[123,168],"drilling":[125],"from":[126],"entire":[127],"cohorts":[128],"patients":[130],"down":[131],"individual":[133],"slices,":[134],"facilitates":[135],"analysis":[137],"relationship":[140,191],"between":[141],"image-derived":[142],"features":[143],"model":[145,224],"performance,":[146,225],"enables":[148],"comparative":[150],"exploration":[151],"outcomes":[155],"models.":[158,262],"All":[159],"realized":[162],"an":[164],"interactive":[165],"interface":[166],"with":[167,228],"linked":[169],"views.":[170],"We":[171],"present":[172],"three":[173],"use":[174],"cases,":[175],"analyzing":[176],"differences":[177],"influence":[184],"size,":[188,208],"other":[193],"data":[194,214],"set":[195],"characteristics":[196],"performance.":[199],"From":[200],"these":[201],"scenarios,":[202],"discovered":[204],"i.e.,":[209],"both":[210],"volumetric":[211],"3D":[213],"pixel":[216],"count":[217],"2D":[219],"data,":[220],"highly":[221],"affects":[222],"samples":[227],"small":[229],"tumors":[230],"often":[231],"yield":[232],"poorer":[233],"results.":[234],"approach":[236],"able":[238],"reveal":[240],"best":[242],"algorithms":[243],"optimal":[246],"configurations":[247],"support":[249],"AI":[250],"obtaining":[253],"more":[254],"insights":[255],"development":[258]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2025-10-10T00:00:00"}