{"id":"https://openalex.org/W4221039706","doi":"https://doi.org/10.1117/12.2612288","title":"Visual attribution for deep learning segmentation in medical imaging","display_name":"Visual attribution for deep learning segmentation in medical imaging","publication_year":2022,"publication_date":"2022-03-31","ids":{"openalex":"https://openalex.org/W4221039706","doi":"https://doi.org/10.1117/12.2612288"},"language":"en","primary_location":{"id":"doi:10.1117/12.2612288","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2612288","pdf_url":null,"source":{"id":"https://openalex.org/S4363607561","display_name":"Medical Imaging 2022: Image Processing","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2022: Image Processing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5005433030","display_name":"Sean Mullan","orcid":"https://orcid.org/0000-0003-4832-4453"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sean Mullan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5018836634","display_name":"Milan Sonka","orcid":"https://orcid.org/0000-0002-9613-9968"},"institutions":[{"id":"https://openalex.org/I126307644","display_name":"University of Iowa","ror":"https://ror.org/036jqmy94","country_code":"US","type":"education","lineage":["https://openalex.org/I126307644"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Milan Sonka","raw_affiliation_strings":["The Univ. of Iowa (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Univ. of Iowa (United States)","institution_ids":["https://openalex.org/I126307644"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.7691,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.88675022,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"25","last_page":"25"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9994999766349792,"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"}},"topics":[{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9994999766349792,"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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"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/T10862","display_name":"AI in cancer detection","score":0.9972000122070312,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7711721658706665},{"id":"https://openalex.org/keywords/attribution","display_name":"Attribution","score":0.7042717337608337},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6786920428276062},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6763160228729248},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.6234549880027771},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.601754903793335},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.547894299030304},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5089962482452393},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.45478546619415283},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.42284488677978516},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1754717230796814},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.07405176758766174}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7711721658706665},{"id":"https://openalex.org/C143299363","wikidata":"https://www.wikidata.org/wiki/Q900584","display_name":"Attribution","level":2,"score":0.7042717337608337},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6786920428276062},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6763160228729248},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6234549880027771},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.601754903793335},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.547894299030304},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5089962482452393},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.45478546619415283},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.42284488677978516},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1754717230796814},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.07405176758766174},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2612288","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2612288","pdf_url":null,"source":{"id":"https://openalex.org/S4363607561","display_name":"Medical Imaging 2022: Image Processing","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2022: Image Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1673923490","https://openalex.org/W1849277567","https://openalex.org/W2761100306","https://openalex.org/W2765793020","https://openalex.org/W2811374795","https://openalex.org/W2943666912","https://openalex.org/W2998175747","https://openalex.org/W3037158619","https://openalex.org/W3096627121","https://openalex.org/W4301409532","https://openalex.org/W6637162671","https://openalex.org/W6639204139","https://openalex.org/W6639824700","https://openalex.org/W6766263406","https://openalex.org/W6772971266","https://openalex.org/W6775305308"],"related_works":["https://openalex.org/W1657880117","https://openalex.org/W2595172197","https://openalex.org/W2127970246","https://openalex.org/W2084856301","https://openalex.org/W1001352512","https://openalex.org/W4382618745","https://openalex.org/W2885125400","https://openalex.org/W1989889224","https://openalex.org/W2748922771","https://openalex.org/W1987128138"],"abstract_inverted_index":{"Despite":[0],"the":[1,30,35,129,133,139,240],"widespread":[2],"use":[3,51,220],"of":[4,26,45,60,84,103,110,196,203,222,242,250],"Convolutional":[5],"Neural":[6],"Networks":[7],"(CNNs)":[8],"for":[9,50,63,71],"segmentation":[10,53,64,218],"in":[11,179],"medical":[12],"imaging,":[13],"there":[14],"is":[15],"yet":[16],"to":[17,22,47,128,137,156,163],"be":[18,48,77,230],"a":[19,57,89,107,176,190,245],"validated":[20],"method":[21,59,102],"determine":[23],"what":[24],"regions":[25,167,221],"input":[27,140,223],"images":[28,141],"inform":[29],"models\u2019":[31],"decisions.":[32],"To":[33,80],"advance":[34],"field,":[36],"we":[37,86],"have":[38],"1)":[39],"modified":[40,207],"three":[41,171],"general,":[42],"prevalent":[43],"methods":[44,83,148,174,213],"classification-attribution":[46,173],"applicable":[49],"with":[52,206],"models,":[54,65],"2)":[55],"developed":[56],"novel":[58,101],"attribution":[61,93,104,135,209],"explicitly":[62],"and":[66,125,142,161,183,198,235,247],"3)":[67],"formulated":[68],"validation":[69,243],"metrics":[70,241],"these":[72],"attributions":[73],"so":[74],"results":[75],"can":[76,228],"quantitatively":[78],"compared.":[79],"adapt":[81],"existing":[82],"classification-attribution,":[85],"newly":[87],"employed":[88],"weighted":[90,108,120],"sum":[91,109],"across":[92,115],"maps":[94,136],"from":[95,112,232],"each":[96,113,122],"post-bottleneck":[97,117],"layer.":[98],"For":[99],"our":[100],"(Kernel-Weighted":[105],"Contribution),":[106],"activations":[111],"kernel":[114],"all":[116],"layers":[118],"was":[119],"by":[121],"kernel\u2019s":[123],"dependent":[124],"independent":[126],"contributions":[127],"segmentation.":[130],"We":[131],"used":[132],"generated":[134],"mask":[138],"generate":[143],"new":[144,212],"predicted":[145],"segmentations.":[146],"The":[147],"were":[149],"then":[150],"scored":[151],"based":[152],"on":[153],"their":[154],"sensitivity":[155],"region":[157],"importance":[158],"(Prediction":[159],"Preserved)":[160],"ability":[162],"only":[164],"attribute":[165],"relevant":[166,226,236],"(Image":[168],"Preserved).":[169],"All":[170],"adapted":[172],"showed":[175,189],"significant":[177],"increase":[178],"both":[180,233],"Prediction":[181,200],"Preserved":[182,185,194,201],"Image":[184,193],"scores.":[186],"Kernel-Weighted":[187],"Contribution":[188],"median":[191],"decreased":[192],"score":[195,202],"2-12%":[197],"increased":[199],"12-21%":[204],"compared":[205],"classification":[208],"methods.":[210,252],"These":[211],"provide":[214],"insight":[215],"into":[216],"how":[217],"models":[219],"images.":[224],"Clinically":[225],"features":[227],"consequently":[229],"extracted":[231],"foreground":[234],"background":[237],"regions.":[238],"Additionally,":[239],"facilitate":[244],"quantitative":[246],"objective":[248],"comparison":[249],"segmentation-attribution":[251]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
