{"id":"https://openalex.org/W4388505231","doi":"https://doi.org/10.1109/tnnls.2023.3326283","title":"MGRW-Transformer: Multigranularity Random Walk Transformer Model for Interpretable Learning","display_name":"MGRW-Transformer: Multigranularity Random Walk Transformer Model for Interpretable Learning","publication_year":2023,"publication_date":"2023-11-08","ids":{"openalex":"https://openalex.org/W4388505231","doi":"https://doi.org/10.1109/tnnls.2023.3326283","pmid":"https://pubmed.ncbi.nlm.nih.gov/37938954"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2023.3326283","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2023.3326283","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5069969191","display_name":"Weiping Ding","orcid":"https://orcid.org/0000-0002-3180-7347"},"institutions":[{"id":"https://openalex.org/I199305430","display_name":"Nantong University","ror":"https://ror.org/02afcvw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I199305430"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiping Ding","raw_affiliation_strings":["School of Computer Science and Technology, Nantong University, Nantong, China"],"raw_orcid":"https://orcid.org/0000-0002-3180-7347","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Nantong University, Nantong, China","institution_ids":["https://openalex.org/I199305430"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100751394","display_name":"Yu Geng","orcid":"https://orcid.org/0000-0001-7615-7950"},"institutions":[{"id":"https://openalex.org/I199305430","display_name":"Nantong University","ror":"https://ror.org/02afcvw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I199305430"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Geng","raw_affiliation_strings":["School of Computer Science and Technology, Nantong University, Nantong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Nantong University, Nantong, China","institution_ids":["https://openalex.org/I199305430"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087093956","display_name":"Jiashuang Huang","orcid":"https://orcid.org/0000-0002-6204-9569"},"institutions":[{"id":"https://openalex.org/I199305430","display_name":"Nantong University","ror":"https://ror.org/02afcvw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I199305430"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiashuang Huang","raw_affiliation_strings":["School of Computer Science and Technology, Nantong University, Nantong, China"],"raw_orcid":"https://orcid.org/0000-0002-6204-9569","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Nantong University, Nantong, China","institution_ids":["https://openalex.org/I199305430"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014589017","display_name":"Hengrong Ju","orcid":"https://orcid.org/0000-0001-9894-9844"},"institutions":[{"id":"https://openalex.org/I199305430","display_name":"Nantong University","ror":"https://ror.org/02afcvw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I199305430"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hengrong Ju","raw_affiliation_strings":["School of Computer Science and Technology, Nantong University, Nantong, China"],"raw_orcid":"https://orcid.org/0000-0001-9894-9844","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Nantong University, Nantong, China","institution_ids":["https://openalex.org/I199305430"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100405751","display_name":"Haipeng Wang","orcid":"https://orcid.org/0000-0001-7957-2957"},"institutions":[{"id":"https://openalex.org/I199305430","display_name":"Nantong University","ror":"https://ror.org/02afcvw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I199305430"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haipeng Wang","raw_affiliation_strings":["School of Computer Science and Technology, Nantong University, Nantong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Nantong University, Nantong, China","institution_ids":["https://openalex.org/I199305430"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058936239","display_name":"Chin\u2010Teng Lin","orcid":"https://orcid.org/0000-0001-8371-8197"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Chin-Teng Lin","raw_affiliation_strings":["Centre for Artificial Intelligence, FEIT, University of Technology Sydney, Ultimo, NSW, Australia"],"raw_orcid":"https://orcid.org/0000-0001-8371-8197","affiliations":[{"raw_affiliation_string":"Centre for Artificial Intelligence, FEIT, University of Technology Sydney, Ultimo, NSW, Australia","institution_ids":["https://openalex.org/I114017466"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.8723,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.85963608,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"36","issue":"1","first_page":"1104","last_page":"1118"},"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":0.992900013923645,"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":0.992900013923645,"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/T10862","display_name":"AI in cancer detection","score":0.9616000056266785,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9506999850273132,"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/transformer","display_name":"Transformer","score":0.5977759957313538},{"id":"https://openalex.org/keywords/random-walk","display_name":"Random walk","score":0.5805770754814148},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.45949792861938477},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3702296316623688},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.22484400868415833},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.20478790998458862},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.19400730729103088},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.11085718870162964},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.056719839572906494}],"concepts":[{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5977759957313538},{"id":"https://openalex.org/C121194460","wikidata":"https://www.wikidata.org/wiki/Q856741","display_name":"Random walk","level":2,"score":0.5805770754814148},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.45949792861938477},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3702296316623688},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.22484400868415833},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20478790998458862},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.19400730729103088},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.11085718870162964},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.056719839572906494}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnnls.2023.3326283","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2023.3326283","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:37938954","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/37938954","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on neural networks and learning systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.550000011920929,"display_name":"Peace, Justice and strong institutions"}],"awards":[{"id":"https://openalex.org/G1591510878","display_name":null,"funder_award_id":"62006128","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3711735600","display_name":null,"funder_award_id":"61976120","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5845899633","display_name":null,"funder_award_id":"2022M711716","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"},{"id":"https://openalex.org/G7476944060","display_name":null,"funder_award_id":"BK20231337","funder_id":"https://openalex.org/F4320322769","funder_display_name":"Natural Science Foundation of Jiangsu Province"},{"id":"https://openalex.org/G8011240538","display_name":null,"funder_award_id":"62102199","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"},{"id":"https://openalex.org/F4320322769","display_name":"Natural Science Foundation of Jiangsu Province","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W1849277567","https://openalex.org/W1998294030","https://openalex.org/W2117539524","https://openalex.org/W2117741877","https://openalex.org/W2125637308","https://openalex.org/W2194775991","https://openalex.org/W2295107390","https://openalex.org/W2336525064","https://openalex.org/W2765793020","https://openalex.org/W2773114958","https://openalex.org/W2801973181","https://openalex.org/W2917767525","https://openalex.org/W2946794439","https://openalex.org/W2950768109","https://openalex.org/W2962858109","https://openalex.org/W2963409068","https://openalex.org/W3006425969","https://openalex.org/W3008264294","https://openalex.org/W3011037064","https://openalex.org/W3035253074","https://openalex.org/W3040502522","https://openalex.org/W3091921149","https://openalex.org/W3092339728","https://openalex.org/W3112949427","https://openalex.org/W3121523901","https://openalex.org/W3127751679","https://openalex.org/W3164485173","https://openalex.org/W3165311856","https://openalex.org/W3172381285","https://openalex.org/W3174522949","https://openalex.org/W3176196997","https://openalex.org/W3203480968","https://openalex.org/W3204166336","https://openalex.org/W3211674809","https://openalex.org/W4255729856","https://openalex.org/W4297463986","https://openalex.org/W4297509724","https://openalex.org/W4385245566","https://openalex.org/W6637373629","https://openalex.org/W6752170072","https://openalex.org/W6780226713","https://openalex.org/W6798693712","https://openalex.org/W6799920274"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W1597743604","https://openalex.org/W2038723108","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W2033630974","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W2382290278","https://openalex.org/W2350741829"],"abstract_inverted_index":{"Deep-learning":[0],"models":[1,22],"have":[2],"been":[3],"widely":[4],"used":[5,61,184],"in":[6,94],"image":[7,49,142,181],"recognition":[8,50,136],"tasks":[9],"due":[10],"to":[11,42,46,76,109,129,150,187,215],"their":[12,36],"strong":[13],"feature-learning":[14],"ability.":[15],"However,":[16,85],"most":[17,59],"of":[18,32,72,91,228,242,248],"the":[19,58,70,131,135,141,151,157,162,168,174,179,193,202,209,217,222,226,229,236,243,249,255],"current":[20],"deep-learning":[21,62,103],"are":[23,183],"\"black":[24],"box\"":[25],"systems":[26],"that":[27,68,133,235,262],"lack":[28],"a":[29,65,102,106,117,197,239,246],"semantic":[30,240],"explanation":[31],"how":[33,254],"they":[34],"reached":[35],"conclusions.":[37,114],"This":[38],"makes":[39,98],"it":[40,99],"difficult":[41,100],"apply":[43],"these":[44],"methods":[45],"complex":[47],"medical":[48,86,269,279],"tasks.":[51],"The":[52,232],"vision":[53],"transformer":[54,121],"(ViT)":[55],"model":[56,63,104,123,237],"is":[57,165,234],"commonly":[60],"with":[64,105,167,268],"self-attention":[66,107],"mechanism":[67,128],"shows":[69],"region":[71],"influence":[73,134],"as":[74,185,196],"compared":[75],"traditional":[77],"convolutional":[78],"networks.":[79],"Thus,":[80],"ViT":[81,152],"offers":[82,238],"greater":[83],"interpretability.":[84],"images":[87,270],"often":[88],"contain":[89],"lesions":[90],"variable":[92],"size":[93],"different":[95],"locations,":[96],"which":[97],"for":[101,154,276],"module":[108,153],"reach":[110],"correct":[111],"and":[112,147,200,220,251],"explainable":[113],"We":[115,206],"propose":[116],"multigranularity":[118,169,175],"random":[119,170,176,204],"walk":[120,171,177],"(MGRW-Transformer)":[122],"guided":[124],"by":[125,278],"an":[126,189,273],"attention":[127,158,194],"find":[130],"regions":[132],"task.":[137],"Our":[138],"method":[139,264],"divides":[140],"into":[143,212,253],"multiple":[144],"subimage":[145],"blocks":[146,182,211],"transfers":[148],"them":[149],"classification.":[155],"Simultaneously,":[156],"matrix":[159],"output":[160],"from":[161],"multiattention":[163],"layer":[164],"fused":[166],"module.":[172],"Within":[173],"module,":[178],"segmented":[180],"nodes":[186],"construct":[188],"undirected":[190],"graph":[191],"using":[192],"node":[195,199],"starting":[198],"guiding":[201],"coarse-grained":[203],"walk.":[205],"appropriately":[207],"divide":[208],"coarse":[210],"finer":[213],"ones":[214],"manage":[216],"computational":[218],"cost":[219],"combine":[221],"results":[223,260],"based":[224],"on":[225],"importance":[227],"discovered":[230],"features.":[231],"result":[233],"interpretation":[241,275],"input":[244],"image,":[245],"visualization":[247],"interpretation,":[250],"insight":[252],"decision":[256],"was":[257],"reached.":[258],"Experimental":[259],"show":[261],"our":[263],"improves":[265],"classification":[266],"performance":[267],"while":[271],"presenting":[272],"understandable":[274],"use":[277],"professionals.":[280]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":2}],"updated_date":"2026-07-25T15:57:00.446498","created_date":"2025-10-10T00:00:00"}