{"id":"https://openalex.org/W7126085569","doi":"https://doi.org/10.1109/bibm66473.2025.11356679","title":"TCM-VisResolve: Multimodal Recognition of Dried Herbs and Clinical MCQ Answering in Traditional Chinese Medicine","display_name":"TCM-VisResolve: Multimodal Recognition of Dried Herbs and Clinical MCQ Answering in Traditional Chinese Medicine","publication_year":2025,"publication_date":"2025-12-15","ids":{"openalex":"https://openalex.org/W7126085569","doi":"https://doi.org/10.1109/bibm66473.2025.11356679"},"language":null,"primary_location":{"id":"doi:10.1109/bibm66473.2025.11356679","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356679","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/A5073985820","display_name":"Wudao Yang","orcid":"https://orcid.org/0000-0001-8411-1450"},"institutions":[{"id":"https://openalex.org/I145897649","display_name":"Minzu University of China","ror":"https://ror.org/0044e2g62","country_code":"CN","type":"education","lineage":["https://openalex.org/I145897649"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wudao Yang","raw_affiliation_strings":["School of Mathematics & Computer Science, Yunnan Minzu University,CS Dept.,Kunming,China"],"affiliations":[{"raw_affiliation_string":"School of Mathematics & Computer Science, Yunnan Minzu University,CS Dept.,Kunming,China","institution_ids":["https://openalex.org/I145897649"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122861390","display_name":"Zhiqiang Yu","orcid":null},"institutions":[{"id":"https://openalex.org/I145897649","display_name":"Minzu University of China","ror":"https://ror.org/0044e2g62","country_code":"CN","type":"education","lineage":["https://openalex.org/I145897649"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiqiang Yu","raw_affiliation_strings":["School of Mathematics & Computer Science, Yunnan Minzu University,CS Dept.,Kunming,China"],"affiliations":[{"raw_affiliation_string":"School of Mathematics & Computer Science, Yunnan Minzu University,CS Dept.,Kunming,China","institution_ids":["https://openalex.org/I145897649"]}]},{"author_position":"last","author":{"id":null,"display_name":"Chee Seng Chan","orcid":null},"institutions":[{"id":"https://openalex.org/I33849332","display_name":"University of Malaya","ror":"https://ror.org/00rzspn62","country_code":"MY","type":"education","lineage":["https://openalex.org/I33849332"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"Chee Seng Chan","raw_affiliation_strings":["Universiti Malaya,Faculty of Comp. Sci. & Info. Tech.,Kuala Lumpur,Malaysia"],"affiliations":[{"raw_affiliation_string":"Universiti Malaya,Faculty of Comp. Sci. & Info. Tech.,Kuala Lumpur,Malaysia","institution_ids":["https://openalex.org/I33849332"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5073985820"],"corresponding_institution_ids":["https://openalex.org/I145897649"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.81202605,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"5296","last_page":"5303"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12647","display_name":"Traditional Chinese Medicine Studies","score":0.5989999771118164,"subfield":{"id":"https://openalex.org/subfields/2707","display_name":"Complementary and alternative medicine"},"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/T12647","display_name":"Traditional Chinese Medicine Studies","score":0.5989999771118164,"subfield":{"id":"https://openalex.org/subfields/2707","display_name":"Complementary and alternative medicine"},"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/T11948","display_name":"Machine Learning in Materials Science","score":0.02410000003874302,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.022600000724196434,"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/overfitting","display_name":"Overfitting","score":0.6291000247001648},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.5069000124931335},{"id":"https://openalex.org/keywords/generalizability-theory","display_name":"Generalizability theory","score":0.453000009059906},{"id":"https://openalex.org/keywords/traditional-chinese-medicine","display_name":"Traditional Chinese medicine","score":0.4406999945640564},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.41609999537467957},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3718000054359436},{"id":"https://openalex.org/keywords/multiple-choice","display_name":"Multiple choice","score":0.3628000020980835}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6976000070571899},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.6291000247001648},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5490999817848206},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5414000153541565},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.5069000124931335},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.453000009059906},{"id":"https://openalex.org/C188947578","wikidata":"https://www.wikidata.org/wiki/Q200253","display_name":"Traditional Chinese medicine","level":3,"score":0.4406999945640564},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.427700012922287},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.41609999537467957},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3718000054359436},{"id":"https://openalex.org/C176730311","wikidata":"https://www.wikidata.org/wiki/Q867668","display_name":"Multiple choice","level":3,"score":0.3628000020980835},{"id":"https://openalex.org/C30038468","wikidata":"https://www.wikidata.org/wiki/Q4354775","display_name":"Memorization","level":2,"score":0.3456000089645386},{"id":"https://openalex.org/C3018428822","wikidata":"https://www.wikidata.org/wiki/Q7850","display_name":"Chinese language","level":2,"score":0.31700000166893005},{"id":"https://openalex.org/C2781181686","wikidata":"https://www.wikidata.org/wiki/Q4226068","display_name":"Coherence (philosophical gambling strategy)","level":2,"score":0.29820001125335693},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.29649999737739563},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2825999855995178},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.2736000120639801},{"id":"https://openalex.org/C2779974597","wikidata":"https://www.wikidata.org/wiki/Q28448986","display_name":"Clinical Practice","level":2,"score":0.2727000117301941},{"id":"https://openalex.org/C2777267654","wikidata":"https://www.wikidata.org/wiki/Q3519023","display_name":"Test (biology)","level":2,"score":0.2667999863624573},{"id":"https://openalex.org/C556039675","wikidata":"https://www.wikidata.org/wiki/Q771035","display_name":"Traditional medicine","level":1,"score":0.2621000111103058}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm66473.2025.11356679","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356679","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":[{"score":0.5228376388549805,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W2111915606","https://openalex.org/W2936823960","https://openalex.org/W4214646989","https://openalex.org/W4313262066","https://openalex.org/W4384944490","https://openalex.org/W4394685827","https://openalex.org/W4401483323","https://openalex.org/W4402188845","https://openalex.org/W4402667093","https://openalex.org/W4404851714"],"related_works":[],"abstract_inverted_index":{"We":[0],"present":[1,76],"TCM-VR,":[2],"a":[3,77],"domain-specific":[4],"multimodal":[5,163],"large":[6],"language":[7],"model":[8,130,168],"(MLLM)":[9],"for":[10,45,143],"Traditional":[11],"Chinese":[12],"Medicine":[13],"(TCM)":[14],"capable":[15],"of":[16,19,35],"recognizing":[17],"images":[18,34],"dried":[20],"herbs":[21,36],"and":[22,29,40,58,64,72,89,101,117,124,153],"answering":[23],"clinical-type":[24],"multiple-choice":[25],"questions":[26],"(MCQs).":[27],"Trained":[28],"finedtuned":[30],"upon":[31],"220":[32],"K":[33,42],"in":[37,61,105,113,146,161],"163":[38],"classes":[39],"880":[41],"candidate":[43],"answers":[44],"MCQs,":[46],"respectively,":[47],"based":[48],"on":[49],"Qwen2.5VL,":[50],"TCM-VR":[51,145],"bridges":[52],"the":[53,98,129],"gap":[54],"between":[55],"visual":[56],"recognition":[57],"symbolic":[59],"reasoning":[60],"TCM":[62],"education":[63],"diagnostics.":[65],"In":[66],"an":[67],"effort":[68],"to":[69,103],"decrease":[70],"overfitting":[71],"respond-position":[73],"bias,":[74],"we":[75],"Cross-Transformation":[78],"Memory":[79],"Mechanism":[80],"(CTMM)":[81],"which":[82,158],"enforces":[83],"semantic":[84],"coherence":[85],"through":[86],"paraphrasing":[87],"prompts":[88],"shuffled":[90],"answer":[91,137],"orders":[92],"at":[93,171],"training":[94],"time.":[95],"This":[96],"enhances":[97],"model's":[99],"generalizability":[100],"robustness":[102],"variations":[104],"input.":[106],"TCMVR":[107],"achieves":[108],"96.7":[109],"%":[110],"MCQ":[111],"accuracy":[112],"held-out":[114],"test":[115],"suites":[116],"significantly":[118],"outperforms":[119],"general-purpose":[120],"models":[121],"like":[122],"GPT-4o":[123],"Gemini.":[125],"Case":[126],"analyses":[127],"confirm":[128],"reason":[131],"beyond":[132],"content":[133],"rather":[134],"than":[135],"memorizing":[136],"locations.":[138],"It":[139],"shows":[140],"favorable":[141],"potential":[142],"using":[144],"learning":[147],"materials,":[148],"clinical":[149],"decision":[150],"support":[151],"systems,":[152,157],"intelligent":[154],"herb":[155],"dispensary":[156],"constitutes":[159],"advancements":[160],"interpretable,":[162],"traditional":[164],"medicine":[165],"AI.":[166],"Our":[167],"is":[169],"available":[170],"https://huggingface.co/dylanyang963/TCM-VR.":[172]},"counts_by_year":[],"updated_date":"2026-03-25T23:56:10.502304","created_date":"2026-01-30T00:00:00"}