{"id":"https://openalex.org/W7126122004","doi":"https://doi.org/10.1109/bibm66473.2025.11357108","title":"MENet: Memory Enhanced Network for Chest Radiology Report Generation","display_name":"MENet: Memory Enhanced Network for Chest Radiology Report Generation","publication_year":2025,"publication_date":"2025-12-15","ids":{"openalex":"https://openalex.org/W7126122004","doi":"https://doi.org/10.1109/bibm66473.2025.11357108"},"language":null,"primary_location":{"id":"doi:10.1109/bibm66473.2025.11357108","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11357108","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","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/A5124268590","display_name":"Chuang Dong","orcid":null},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chuang Dong","raw_affiliation_strings":["School of Computer Science and Engineering, Northeastern University,Shenyang,China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Northeastern University,Shenyang,China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100406237","display_name":"Shanshan Wang","orcid":"https://orcid.org/0000-0002-0575-6523"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shanshan Wang","raw_affiliation_strings":["School of Computer Science and Engineering, Northeastern University,Shenyang,China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Northeastern University,Shenyang,China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124229077","display_name":"Gang Li","orcid":null},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gang Li","raw_affiliation_strings":["School of Computer Science and Engineering, Northeastern University,Shenyang,China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Northeastern University,Shenyang,China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124196978","display_name":"Cong Ba","orcid":null},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cong Ba","raw_affiliation_strings":["School of Computer Science and Engineering, Northeastern University,Shenyang,China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Northeastern University,Shenyang,China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5124208664","display_name":"Wei Li","orcid":null},"institutions":[{"id":"https://openalex.org/I1327237609","display_name":"Ministry of Education of the People's Republic of China","ror":"https://ror.org/01mv9t934","country_code":"CN","type":"government","lineage":["https://openalex.org/I1327237609","https://openalex.org/I4210127390"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Li","raw_affiliation_strings":["Key Laboratory of Intelligent Computing in Medical Image (MIIC) Ministry of Education,Shenyang,China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Intelligent Computing in Medical Image (MIIC) Ministry of Education,Shenyang,China","institution_ids":["https://openalex.org/I1327237609"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5124268590"],"corresponding_institution_ids":["https://openalex.org/I9224756"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.73271312,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"6240","last_page":"6247"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.8023999929428101,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.8023999929428101,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.05209999904036522,"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/T11894","display_name":"Radiology practices and education","score":0.02759999968111515,"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/correctness","display_name":"Correctness","score":0.729200005531311},{"id":"https://openalex.org/keywords/workload","display_name":"Workload","score":0.6100000143051147},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5817999839782715},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.5504000186920166},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4487000107765198},{"id":"https://openalex.org/keywords/abnormality","display_name":"Abnormality","score":0.4092999994754791},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.3596000075340271},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.31060001254081726}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7689999938011169},{"id":"https://openalex.org/C55439883","wikidata":"https://www.wikidata.org/wiki/Q360812","display_name":"Correctness","level":2,"score":0.729200005531311},{"id":"https://openalex.org/C2778476105","wikidata":"https://www.wikidata.org/wiki/Q628539","display_name":"Workload","level":2,"score":0.6100000143051147},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5817999839782715},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.5504000186920166},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4828000068664551},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4487000107765198},{"id":"https://openalex.org/C50965678","wikidata":"https://www.wikidata.org/wiki/Q2724302","display_name":"Abnormality","level":2,"score":0.4092999994754791},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.3596000075340271},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3285999894142151},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.32339999079704285},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.31850001215934753},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.31060001254081726},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.3100999891757965},{"id":"https://openalex.org/C2993807640","wikidata":"https://www.wikidata.org/wiki/Q103709453","display_name":"Attention network","level":2,"score":0.2971000075340271},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.2928999960422516},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.27309998869895935},{"id":"https://openalex.org/C58328972","wikidata":"https://www.wikidata.org/wiki/Q184609","display_name":"Expert system","level":2,"score":0.2655999958515167},{"id":"https://openalex.org/C2985722590","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medical knowledge","level":2,"score":0.2653999924659729},{"id":"https://openalex.org/C19527891","wikidata":"https://www.wikidata.org/wiki/Q1120908","display_name":"Medical physics","level":1,"score":0.26440000534057617},{"id":"https://openalex.org/C534262118","wikidata":"https://www.wikidata.org/wiki/Q177719","display_name":"Medical diagnosis","level":2,"score":0.2567000091075897},{"id":"https://openalex.org/C2779974597","wikidata":"https://www.wikidata.org/wiki/Q28448986","display_name":"Clinical Practice","level":2,"score":0.25429999828338623}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm66473.2025.11357108","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11357108","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","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":23,"referenced_works":["https://openalex.org/W1956340063","https://openalex.org/W2101105183","https://openalex.org/W2152772232","https://openalex.org/W2168199261","https://openalex.org/W2194775991","https://openalex.org/W2888321701","https://openalex.org/W2995225687","https://openalex.org/W2997704374","https://openalex.org/W3035284526","https://openalex.org/W3074741277","https://openalex.org/W3104609094","https://openalex.org/W3151410070","https://openalex.org/W3177048142","https://openalex.org/W3181252431","https://openalex.org/W4281729070","https://openalex.org/W4285531589","https://openalex.org/W4360604432","https://openalex.org/W4390849994","https://openalex.org/W4390905318","https://openalex.org/W4394597485","https://openalex.org/W4395096791","https://openalex.org/W4406602253","https://openalex.org/W4410461169"],"related_works":[],"abstract_inverted_index":{"Automatically":[0],"generating":[1],"radiology":[2],"reports":[3],"from":[4],"chest":[5],"X-ray":[6],"images":[7],"can":[8],"substantially":[9],"reduce":[10],"the":[11,46,66,109,125,137,166,177],"workload":[12],"of":[13,68,139,183],"radiologists":[14],"and":[15,30,40,60,103,119,142,159,168,180],"improve":[16],"diagnostic":[17],"efficiency.":[18],"However,":[19],"existing":[20],"methods":[21],"face":[22],"two":[23,146],"key":[24],"challenges.":[25],"First,":[26],"due":[27],"to":[28,77,135],"visual":[29],"textual":[31,178],"information":[32],"bias,":[33],"models":[34],"often":[35],"focus":[36],"on":[37,165],"non-abnormal":[38],"regions":[39],"produce":[41],"generic":[42],"descriptions,":[43],"while":[44,124],"neglecting":[45],"abnormal":[47,122],"areas":[48],"that":[49,99,172],"are":[50,148],"critical":[51],"for":[52],"diagnosis.":[53],"This":[54],"results":[55,164],"in":[56,85],"limited":[57],"clinical":[58,181],"accuracy":[59],"poor":[61],"abnormality":[62],"awareness.":[63],"Second,":[64],"without":[65],"calibration":[67],"prior":[69,104],"expert":[70,133],"knowledge,":[71],"purely":[72],"data-driven":[73],"encoder-decoder":[74,153],"approaches":[75],"struggle":[76],"generate":[78],"clinically":[79,140],"meaningful":[80],"reports,":[81],"limiting":[82],"their":[83],"applicability":[84],"realworld":[86],"scenarios.":[87],"To":[88],"address":[89],"these":[90],"challenges,":[91],"we":[92],"propose":[93],"a":[94],"Memory":[95,111,127],"Enhanced":[96],"Network":[97,113,129],"(MENet)":[98],"integrates":[100],"both":[101,176],"common":[102],"memory":[105],"enhancement":[106],"mechanisms.":[107],"Specifically,":[108],"Common":[110],"Enhancement":[112,128],"(CMENet)":[114],"provides":[115],"general":[116],"medical":[117],"knowledge":[118,134],"helps":[120],"highlight":[121],"regions,":[123],"Prior":[126],"(PMENet)":[130],"retrieves":[131],"structured":[132],"ensure":[136],"generation":[138],"coherent":[141],"faithful":[143],"reports.":[144,185],"These":[145],"modules":[147],"jointly":[149],"optimized":[150],"within":[151],"an":[152],"framework":[154],"using":[155],"contrastive":[156],"learning,":[157],"matching,":[158],"language":[160],"modeling":[161],"objectives.":[162],"Experimental":[163],"IU-Xray":[167],"MIMIC-CXR":[169],"datasets":[170],"demonstrate":[171],"MENet":[173],"consistently":[174],"improves":[175],"quality":[179],"correctness":[182],"generated":[184]},"counts_by_year":[],"updated_date":"2026-02-01T03:34:12.195049","created_date":"2026-01-30T00:00:00"}
