{"id":"https://openalex.org/W7126040201","doi":"https://doi.org/10.1109/bibm66473.2025.11356458","title":"BiFormer-Seg: Dual-Branch Visual Encoding and Text-Guided Decoding for Radiological Segmentation","display_name":"BiFormer-Seg: Dual-Branch Visual Encoding and Text-Guided Decoding for Radiological Segmentation","publication_year":2025,"publication_date":"2025-12-15","ids":{"openalex":"https://openalex.org/W7126040201","doi":"https://doi.org/10.1109/bibm66473.2025.11356458"},"language":null,"primary_location":{"id":"doi:10.1109/bibm66473.2025.11356458","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356458","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/A5124204072","display_name":"Chenxi Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210123849","display_name":"Measurement Technology Northwest (United States)","ror":"https://ror.org/035kh8h98","country_code":"US","type":"company","lineage":["https://openalex.org/I4210123849"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Chenxi Yang","raw_affiliation_strings":["Measurement and Control Technology and Instrument, University of Electronic Science and Technology of China"],"affiliations":[{"raw_affiliation_string":"Measurement and Control Technology and Instrument, University of Electronic Science and Technology of China","institution_ids":["https://openalex.org/I4210123849"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124211346","display_name":"Jiesen Shi","orcid":null},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiesen Shi","raw_affiliation_strings":["School of Electrical Engineering and AI (EEAI School), Xiamen University"],"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering and AI (EEAI School), Xiamen University","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043276259","display_name":"Hao Gong","orcid":"https://orcid.org/0000-0002-1102-9189"},"institutions":[{"id":"https://openalex.org/I4210099069","display_name":"Institute of Acoustics","ror":"https://ror.org/00v8rqv75","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210099069"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hao Gong","raw_affiliation_strings":["Institute of Acoustics, University of Chinese Academy of Sciences"],"affiliations":[{"raw_affiliation_string":"Institute of Acoustics, University of Chinese Academy of Sciences","institution_ids":["https://openalex.org/I4210099069"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124288888","display_name":"Yunpeng Duan","orcid":null},"institutions":[{"id":"https://openalex.org/I38877650","display_name":"Zhengzhou University","ror":"https://ror.org/04ypx8c21","country_code":"CN","type":"education","lineage":["https://openalex.org/I38877650"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunpeng Duan","raw_affiliation_strings":["School of Computer and Artificial Intelligence, Zhengzhou University"],"affiliations":[{"raw_affiliation_string":"School of Computer and Artificial Intelligence, Zhengzhou University","institution_ids":["https://openalex.org/I38877650"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5124218327","display_name":"Liuhao Yun","orcid":null},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liuhao Yun","raw_affiliation_strings":["School of Information Science and Technology, Fudan University"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Technology, Fudan University","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5124204072"],"corresponding_institution_ids":["https://openalex.org/I4210123849"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.73139752,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"6441","last_page":"6448"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.8852999806404114,"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":0.8852999806404114,"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":0.034699998795986176,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.023900000378489494,"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/segmentation","display_name":"Segmentation","score":0.6829000115394592},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.6811000108718872},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.6176999807357788},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.6089000105857849},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5821999907493591},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4341000020503998},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4311000108718872},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.3628000020980835}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7664999961853027},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7317000031471252},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6829000115394592},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.6811000108718872},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.6176999807357788},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.6089000105857849},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5821999907493591},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.505299985408783},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4341000020503998},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4311000108718872},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.3628000020980835},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.32820001244544983},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.3179999887943268},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.3174000084400177},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3005000054836273},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2865999937057495},{"id":"https://openalex.org/C64754055","wikidata":"https://www.wikidata.org/wiki/Q7574053","display_name":"Spatial contextual awareness","level":2,"score":0.28519999980926514},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.28360000252723694},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2653999924659729},{"id":"https://openalex.org/C190892606","wikidata":"https://www.wikidata.org/wiki/Q3512464","display_name":"Radiological weapon","level":2,"score":0.2590000033378601}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm66473.2025.11356458","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356458","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":[{"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8","score":0.44646573066711426}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W2021088830","https://openalex.org/W2592929672","https://openalex.org/W2921406441","https://openalex.org/W2928133111","https://openalex.org/W2964227007","https://openalex.org/W2971013993","https://openalex.org/W2979708377","https://openalex.org/W2979907638","https://openalex.org/W2996290406","https://openalex.org/W3014974815","https://openalex.org/W3016836174","https://openalex.org/W3041333867","https://openalex.org/W3120430728","https://openalex.org/W3171581326","https://openalex.org/W3201906559","https://openalex.org/W3203841574","https://openalex.org/W3204255739","https://openalex.org/W4205480269","https://openalex.org/W4212875960","https://openalex.org/W4295915728","https://openalex.org/W4308235625","https://openalex.org/W4312533035","https://openalex.org/W4321232185","https://openalex.org/W4372260366","https://openalex.org/W4382998948","https://openalex.org/W4385573131","https://openalex.org/W4387211388","https://openalex.org/W4387211904","https://openalex.org/W4390345094","https://openalex.org/W4390874575","https://openalex.org/W4391109864","https://openalex.org/W4400679603","https://openalex.org/W4403067037","https://openalex.org/W4406259725","https://openalex.org/W4406259772","https://openalex.org/W4408353153","https://openalex.org/W4412438604"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"segmentation":[1,78,148],"of":[2,182],"pulmonary":[3],"infection":[4],"regions":[5],"is":[6],"critical":[7],"for":[8,156],"diagnosing":[9],"respiratory":[10],"diseases":[11],"such":[12],"as":[13],"COVID-19":[14],"and":[15,60,86,136,163,175],"pneumonia.":[16],"Although":[17],"recent":[18],"deep":[19],"learning":[20],"approaches":[21,52],"have":[22],"achieved":[23],"promising":[24],"results,":[25],"most":[26],"existing":[27],"methods":[28],"are":[29,126],"either":[30],"unimodal-relying":[31],"solely":[32],"on":[33,160],"visual":[34,97],"information":[35],"with":[36,107],"labor-intensive":[37],"spatial":[38],"annotations-or":[39],"multimodal":[40,77,177],"but":[41],"constrained":[42],"by":[43,111],"rigid":[44],"prompt":[45],"designs":[46],"or":[47,142],"structured":[48],"textual":[49],"templates.":[50],"These":[51],"struggle":[53],"to":[54],"generalize":[55],"across":[56],"heterogeneous":[57],"imaging":[58],"conditions":[59],"diverse":[61],"clinical":[62,90],"narratives,":[63],"limiting":[64],"their":[65,87],"real-world":[66],"applicability.":[67],"To":[68],"address":[69],"these":[70],"challenges,":[71],"we":[72],"propose":[73],"a":[74,95,112,118],"unified,":[75],"prompt-free":[76],"framework":[79],"that":[80,99,167],"jointly":[81],"leverages":[82],"chest":[83],"radiological":[84],"images":[85],"associated":[88],"free-text":[89],"reports.":[91],"Our":[92],"model":[93],"features":[94],"dual-branch":[96],"encoder":[98,120],"fuses":[100],"local":[101],"texture":[102],"representations":[103],"from":[104],"convolutional":[105],"layers":[106],"global":[108],"context":[109],"captured":[110],"pretrained":[113],"Vision":[114],"Transformer.":[115],"In":[116],"parallel,":[117],"text":[119],"processes":[121],"unstructured":[122],"radiology":[123],"reports,":[124],"which":[125],"integrated":[127],"into":[128],"the":[129,145,161,180],"decoding":[130],"stage":[131],"via":[132],"iterative":[133],"cross-modal":[134],"attention":[135],"semantic-guided":[137],"modulation.":[138],"Without":[139],"requiring":[140],"point-":[141],"box-level":[143],"prompts,":[144],"decoder":[146],"produces":[147],"masks":[149],"alongside":[150],"an":[151],"auxiliary":[152],"IoU":[153],"estimation":[154],"head":[155],"confidence-aware":[157],"prediction.":[158],"Experiments":[159],"QaTa-COV19":[162],"MosMedData":[164],"datasets":[165],"demonstrate":[166],"our":[168],"method":[169],"yields":[170],"consistent":[171],"improvements":[172],"over":[173],"vision-only":[174],"prior":[176],"baselines,":[178],"highlighting":[179],"benefits":[181],"semantic":[183],"guidance":[184],"in":[185],"medical":[186],"image":[187],"segmentation.":[188]},"counts_by_year":[],"updated_date":"2026-02-01T03:34:12.195049","created_date":"2026-01-30T00:00:00"}