{"id":"https://openalex.org/W4385584342","doi":"https://doi.org/10.3233/faia230148","title":"Chapter 16. Discovering Visual Concepts and Rules in Convolutional Neural Networks","display_name":"Chapter 16. Discovering Visual Concepts and Rules in Convolutional Neural Networks","publication_year":2023,"publication_date":"2023-07-21","ids":{"openalex":"https://openalex.org/W4385584342","doi":"https://doi.org/10.3233/faia230148"},"language":"en","primary_location":{"id":"doi:10.3233/faia230148","is_oa":false,"landing_page_url":"http://dx.doi.org/10.3233/faia230148","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},"type":"book-chapter","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/A5029919865","display_name":"Joe Townsend","orcid":"https://orcid.org/0000-0002-5478-0028"},"institutions":[{"id":"https://openalex.org/I2252096349","display_name":"Fujitsu (Japan)","ror":"https://ror.org/038e2g226","country_code":"JP","type":"company","lineage":["https://openalex.org/I2252096349"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Joe Townsend","raw_affiliation_strings":["Fujitsu Research of Europe LTD"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fujitsu Research of Europe LTD","institution_ids":["https://openalex.org/I2252096349"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032008577","display_name":"Esma Mansouri-Benssassi","orcid":"https://orcid.org/0000-0003-3510-8129"},"institutions":[{"id":"https://openalex.org/I2252096349","display_name":"Fujitsu (Japan)","ror":"https://ror.org/038e2g226","country_code":"JP","type":"company","lineage":["https://openalex.org/I2252096349"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Esma Mansouri-Benssassi","raw_affiliation_strings":["Fujitsu Research of Europe LTD"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fujitsu Research of Europe LTD","institution_ids":["https://openalex.org/I2252096349"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064727846","display_name":"Kwun Ho Ngan","orcid":"https://orcid.org/0000-0001-7623-942X"},"institutions":[{"id":"https://openalex.org/I180825142","display_name":"City, University of London","ror":"https://ror.org/04489at23","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I180825142"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Kwun Ho Ngan","raw_affiliation_strings":["City University of London"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"City University of London","institution_ids":["https://openalex.org/I180825142"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060005929","display_name":"Artur d\u2019Avila Garcez","orcid":"https://orcid.org/0000-0001-7375-9518"},"institutions":[{"id":"https://openalex.org/I180825142","display_name":"City, University of London","ror":"https://ror.org/04489at23","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I180825142"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Artur d\u2019Avila Garcez","raw_affiliation_strings":["City University of London"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"City University of London","institution_ids":["https://openalex.org/I180825142"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5029919865"],"corresponding_institution_ids":["https://openalex.org/I2252096349"],"apc_list":null,"apc_paid":null,"fwci":1.0109,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.77068618,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.5296000242233276,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.5296000242233276,"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":0.4510999917984009,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7633597254753113},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6113966703414917},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5930894613265991},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5714662671089172},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.5375978350639343},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.517561137676239},{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.5091056227684021},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4809224307537079},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.45533010363578796},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4145696759223938},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3912646472454071},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.2862553596496582},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15578153729438782},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10821360349655151}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7633597254753113},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6113966703414917},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5930894613265991},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5714662671089172},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.5375978350639343},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.517561137676239},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.5091056227684021},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4809224307537079},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45533010363578796},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4145696759223938},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3912646472454071},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2862553596496582},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15578153729438782},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10821360349655151},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/faia230148","is_oa":false,"landing_page_url":"http://dx.doi.org/10.3233/faia230148","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.8100000023841858,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2950475743","https://openalex.org/W4386603768","https://openalex.org/W2886711096","https://openalex.org/W4380078352","https://openalex.org/W3046591097","https://openalex.org/W2590796488","https://openalex.org/W3036048022","https://openalex.org/W4309224979","https://openalex.org/W3026879719","https://openalex.org/W2971473720"],"abstract_inverted_index":{"This":[0,208],"chapter":[1,209],"provides":[2],"an":[3],"overview":[4],"of":[5,17,31,45,61,84,115,138,188,229,236],"ERIC":[6,27,79,122,183,237],"(Extracting":[7],"Relations":[8],"Inferred":[9],"from":[10,22],"Convolutions),":[11],"a":[12,38,43,75,109,133,150,155,174,226],"solution":[13],"to":[14,37,49,89,107,118,125,140,149,173],"the":[15,29,62,66,72,82,113,119,161,180,186,234],"extraction":[16],"explanations":[18],"and":[19,131,232],"human-comprehensible":[20],"knowledge":[21,98],"Convolutional":[23],"Neural":[24],"Networks":[25],"(CNNs).":[26],"reduces":[28],"behaviour":[30],"one":[32],"or":[33,199],"more":[34,240],"convolutional":[35,51],"layers":[36],"discrete":[39],"logic":[40,67],"program":[41],"over":[42],"set":[44,228],"atoms,":[46],"each":[47,221],"corresponding":[48],"individual":[50],"kernels.":[52],"Extracted":[53],"programs":[54,117],"yield":[55,126],"performances":[56],"that":[57,121,194,220],"correlate":[58],"with":[59,96,103,225],"those":[60,93],"original":[63],"model.":[64],"When":[65],"rules":[68,242],"are":[69,195],"analysed":[70],"alongside":[71],"data":[73],"as":[74,100],"visual":[76],"concept":[77],"learner,":[78],"has":[80,184],"demonstrated":[81,185],"discovery":[83],"relevant":[85],"concepts":[86,145,166,190],"when":[87],"applied":[88],"classification":[90,136,176],"tasks,":[91],"including":[92],"in":[94,132,160,179,204],"fields":[95],"specialised":[97],"such":[99],"radiology.":[101],"Concepts":[102],"sharper":[104],"edges":[105],"seem":[106],"have":[108],"positive":[110],"influence":[111],"on":[112,129,154],"fidelity":[114,128],"extracted":[116,144],"extent":[120],"was":[123],"able":[124],"high":[127,245],"MNIST":[130],"traffic":[134],"sign":[135],"task":[137],"up":[139],"43":[141],"classes.":[142],"Also,":[143],"may":[146],"be":[147],"transferred":[148],"different":[151,158],"CNN":[152],"trained":[153],"related":[156],"but":[157],"problem":[159],"same":[162],"domain.":[163],"For":[164],"example,":[165],"identified":[167],"for":[168],"pleural":[169],"effusion":[170],"were":[171],"transferable":[172],"COVID-19":[175],"task.":[177],"Also":[178],"medical":[181,202],"domain,":[182],"capability":[187],"identifying":[189],"used":[191,200],"by":[192,201,238],"CNNs":[193,218],"not":[196],"justified":[197],"anatomically":[198],"doctors":[203],"their":[205],"decision":[206],"making.":[207],"also":[210],"briefly":[211],"reviews":[212],"Elite":[213],"Backpropagation":[214],"(EBP),":[215],"which":[216],"trains":[217],"so":[219],"class":[222],"is":[223],"associated":[224],"small":[227],"elite":[230],"kernels":[231],"improves":[233],"performance":[235],"inducing":[239],"compact":[241],"while":[243],"maintaining":[244],"fidelity.":[246]},"counts_by_year":[{"year":2024,"cited_by_count":2}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}