{"id":"https://openalex.org/W4206048141","doi":"https://doi.org/10.1109/bibm52615.2021.9669626","title":"Weakly Guided Hierarchical Encoder-Decoder Network for Brain CT Report Generation","display_name":"Weakly Guided Hierarchical Encoder-Decoder Network for Brain CT Report Generation","publication_year":2021,"publication_date":"2021-12-09","ids":{"openalex":"https://openalex.org/W4206048141","doi":"https://doi.org/10.1109/bibm52615.2021.9669626"},"language":"en","primary_location":{"id":"doi:10.1109/bibm52615.2021.9669626","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm52615.2021.9669626","pdf_url":null,"source":{"id":"https://openalex.org/S4363607735","display_name":"2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","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":"2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5081134218","display_name":"Sisi Yang","orcid":"https://orcid.org/0009-0008-2696-9107"},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sisi Yang","raw_affiliation_strings":["Faculty of Information Technology, Beijing University of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Information Technology, Beijing University of Technology, Beijing, China","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035117617","display_name":"Junzhong Ji","orcid":"https://orcid.org/0000-0001-6951-741X"},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junzhong Ji","raw_affiliation_strings":["Faculty of Information Technology, Beijing University of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Information Technology, Beijing University of Technology, Beijing, China","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100356742","display_name":"Xiaodan Zhang","orcid":"https://orcid.org/0000-0001-7002-5447"},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaodan Zhang","raw_affiliation_strings":["Faculty of Information Technology, Beijing University of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Information Technology, Beijing University of Technology, Beijing, China","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100414124","display_name":"Ying Liu","orcid":"https://orcid.org/0000-0002-0337-277X"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]},{"id":"https://openalex.org/I4210141942","display_name":"Peking University Third Hospital","ror":"https://ror.org/04wwqze12","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210141942"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ying Liu","raw_affiliation_strings":["Peking University Third Hospital, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking University Third Hospital, Beijing, China","institution_ids":["https://openalex.org/I20231570","https://openalex.org/I4210141942"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100401109","display_name":"Zheng Wang","orcid":"https://orcid.org/0000-0002-6753-6569"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]},{"id":"https://openalex.org/I4210141942","display_name":"Peking University Third Hospital","ror":"https://ror.org/04wwqze12","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210141942"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zheng Wang","raw_affiliation_strings":["Peking University Third Hospital, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking University Third Hospital, Beijing, China","institution_ids":["https://openalex.org/I20231570","https://openalex.org/I4210141942"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"568","last_page":"573"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998000264167786,"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/T10028","display_name":"Topic Modeling","score":0.9998000264167786,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9993000030517578,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9961000084877014,"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/computer-science","display_name":"Computer science","score":0.6857112646102905},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.6572843194007874}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6857112646102905},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.6572843194007874},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm52615.2021.9669626","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm52615.2021.9669626","pdf_url":null,"source":{"id":"https://openalex.org/S4363607735","display_name":"2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","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":"2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.6800000071525574}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1497599070","https://openalex.org/W1514535095","https://openalex.org/W1895577753","https://openalex.org/W1905882502","https://openalex.org/W1956340063","https://openalex.org/W2064675550","https://openalex.org/W2133512280","https://openalex.org/W2135726080","https://openalex.org/W2154652894","https://openalex.org/W2194775991","https://openalex.org/W2334763311","https://openalex.org/W2549599535","https://openalex.org/W2745461083","https://openalex.org/W2770165365","https://openalex.org/W2890888035","https://openalex.org/W2962858109","https://openalex.org/W2963084599","https://openalex.org/W2963409068","https://openalex.org/W2963967185","https://openalex.org/W2968101724","https://openalex.org/W3003817677","https://openalex.org/W3027914507","https://openalex.org/W3098325931","https://openalex.org/W6630875275","https://openalex.org/W6682631176","https://openalex.org/W6898505805"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"Report-writing":[0],"for":[1,11,21,68,117,134],"Brain":[2,70,81,95,118,138,196],"Computed":[3],"Tomography":[4],"(CT)":[5],"imaging":[6,72],"is":[7,17,63],"a":[8,128,145,174],"routine":[9],"procedure":[10],"diagnosing":[12],"cerebrovascular":[13],"diseases,":[14],"while":[15],"it":[16],"time-consuming":[18],"and":[19,38,49,73,114,137,159],"tedious":[20],"radiologists":[22],"especially":[23],"in":[24,57,86,151,180],"highly":[25],"populated":[26],"areas.":[27,169],"Automatic":[28],"report":[29,59,65,115,140],"generation":[30,66,116],"has":[31,53],"the":[32,40,44,69,77,94,108,155,163,186,200,203],"potential":[33],"to":[34,153,182],"alleviate":[35],"radiologists\u2019":[36],"workload":[37],"reduce":[39],"diagnose":[41],"error.":[42],"Currently,":[43],"development":[45],"of":[46,166,189,202],"image":[47,51],"captioning":[48],"medical":[50,58,103],"processing":[52],"driven":[54],"great":[55],"achievements":[56],"generation.":[60,141],"However,":[61],"there":[62],"no":[64],"study":[67],"CT":[71,82,96,119,139,197],"this":[74],"task":[75],"faces":[76],"following":[78],"challenges:":[79],"First,":[80],"lesions":[83,112,135,168,191],"are":[84,98],"disperse":[85],"3-D":[87],"space,":[88],"with":[89,101,123],"more":[90],"morphological":[91],"instability.":[92],"Second,":[93],"reports":[97],"long":[99],"paragraphs":[100,184],"similar":[102],"term.":[104],"These":[105],"challenges":[106],"increase":[107],"difficulty":[109],"o":[110],"f":[111],"recognition":[113],"imaging.":[120],"To":[121],"cope":[122],"these":[124],"challenges,":[125],"we":[126,143,172],"propose":[127,144,173],"weakly":[129,146],"guided":[130,147],"hierarchical":[131],"encoder-decoder":[132],"network":[133,178],"learning":[136],"Specifically,":[142],"attention":[148],"model":[149],"(WGAM)":[150],"encoder":[152],"capture":[154],"most":[156],"important":[157],"areas":[158],"scans":[160],"gradually":[161],"under":[162,185],"weak":[164,187],"guidance":[165,188],"possible":[167,190],"In":[170],"addition,":[171],"keywords-driven":[175],"interactive":[176],"recurrent":[177],"(KIRN)":[179],"decoder":[181],"generate":[183],"keywords.":[192],"Experiments":[193],"on":[194],"our":[195],"dataset":[198],"demonstrate":[199],"effectiveness":[201],"proposed":[204],"method.":[205]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
