{"id":"https://openalex.org/W4417473380","doi":"https://doi.org/10.1109/sipaim67325.2025.11283313","title":"Improving the performance of multimodal large language models on histopathology images classification","display_name":"Improving the performance of multimodal large language models on histopathology images classification","publication_year":2025,"publication_date":"2025-11-18","ids":{"openalex":"https://openalex.org/W4417473380","doi":"https://doi.org/10.1109/sipaim67325.2025.11283313"},"language":null,"primary_location":{"id":"doi:10.1109/sipaim67325.2025.11283313","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sipaim67325.2025.11283313","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 21st International Symposium on Biomedical Image Processing and Analysis (SIPAIM)","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/A5120847989","display_name":"Jos\u00e9 J. Sierra-Molina","orcid":null},"institutions":[{"id":"https://openalex.org/I36243813","display_name":"Universidad Nacional de Colombia","ror":"https://ror.org/059yx9a68","country_code":"CO","type":"education","lineage":["https://openalex.org/I36243813"]}],"countries":["CO"],"is_corresponding":true,"raw_author_name":"Jos\u00e9 J. Sierra-Molina","raw_affiliation_strings":["Universidad Nacional de Colombia,MindLab,Depto. de Ing. de Sistemas e Industrial,Bogot&#x00E1;,Colombia"],"affiliations":[{"raw_affiliation_string":"Universidad Nacional de Colombia,MindLab,Depto. de Ing. de Sistemas e Industrial,Bogot&#x00E1;,Colombia","institution_ids":["https://openalex.org/I36243813"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024581256","display_name":"Andr\u00e9s Rosso-Mateus","orcid":null},"institutions":[{"id":"https://openalex.org/I36243813","display_name":"Universidad Nacional de Colombia","ror":"https://ror.org/059yx9a68","country_code":"CO","type":"education","lineage":["https://openalex.org/I36243813"]}],"countries":["CO"],"is_corresponding":false,"raw_author_name":"Andr\u00e9s Rosso-Mateus","raw_affiliation_strings":["Universidad Nacional de Colombia,MindLab,Depto. de Ing. de Sistemas e Industrial,Bogot&#x00E1;,Colombia"],"affiliations":[{"raw_affiliation_string":"Universidad Nacional de Colombia,MindLab,Depto. de Ing. de Sistemas e Industrial,Bogot&#x00E1;,Colombia","institution_ids":["https://openalex.org/I36243813"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080973347","display_name":"Fabio A. Gonz\u00e1lez","orcid":"https://orcid.org/0000-0001-9009-7288"},"institutions":[{"id":"https://openalex.org/I36243813","display_name":"Universidad Nacional de Colombia","ror":"https://ror.org/059yx9a68","country_code":"CO","type":"education","lineage":["https://openalex.org/I36243813"]}],"countries":["CO"],"is_corresponding":false,"raw_author_name":"Fabio A. Gonz\u00e1lez","raw_affiliation_strings":["Universidad Nacional de Colombia,MindLab,Depto. de Ing. de Sistemas e Industrial,Bogot&#x00E1;,Colombia"],"affiliations":[{"raw_affiliation_string":"Universidad Nacional de Colombia,MindLab,Depto. de Ing. de Sistemas e Industrial,Bogot&#x00E1;,Colombia","institution_ids":["https://openalex.org/I36243813"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5120847989"],"corresponding_institution_ids":["https://openalex.org/I36243813"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.22744496,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.761900007724762,"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/T10862","display_name":"AI in cancer detection","score":0.761900007724762,"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":0.04769999906420708,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.019300000742077827,"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/interpretability","display_name":"Interpretability","score":0.9104999899864197},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4731000065803528},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.38519999384880066},{"id":"https://openalex.org/keywords/explanatory-power","display_name":"Explanatory power","score":0.37959998846054077},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.37610000371932983},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.3361999988555908},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.3260999917984009},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.32019999623298645}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.9104999899864197},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7587000131607056},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6610000133514404},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4844000041484833},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4731000065803528},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43479999899864197},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.38519999384880066},{"id":"https://openalex.org/C2777402642","wikidata":"https://www.wikidata.org/wiki/Q2557224","display_name":"Explanatory power","level":2,"score":0.37959998846054077},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.37610000371932983},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3361999988555908},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.3260999917984009},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.32019999623298645},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.31520000100135803},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2930999994277954},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.2906000018119812},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.289000004529953},{"id":"https://openalex.org/C2780910867","wikidata":"https://www.wikidata.org/wiki/Q1952416","display_name":"Multimodality","level":2,"score":0.2849000096321106},{"id":"https://openalex.org/C534262118","wikidata":"https://www.wikidata.org/wiki/Q177719","display_name":"Medical diagnosis","level":2,"score":0.2782999873161316},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.2712000012397766},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.2671000063419342},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2655999958515167},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.259799987077713},{"id":"https://openalex.org/C2983448237","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Language understanding","level":2,"score":0.259799987077713},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.2583000063896179}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/sipaim67325.2025.11283313","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sipaim67325.2025.11283313","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 21st International Symposium on Biomedical Image Processing and Analysis (SIPAIM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":4,"referenced_works":["https://openalex.org/W3173898803","https://openalex.org/W4402701904","https://openalex.org/W4404584849","https://openalex.org/W4409205383"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"diagnosis":[1],"of":[2,103,131],"lesions":[3],"in":[4,43,55,70],"histopathological":[5],"images":[6,107],"requires":[7],"experts":[8],"with":[9,123],"extensive":[10],"experience.":[11],"In":[12,75],"some":[13],"cases,":[14],"distinguishing":[15],"between":[16],"hyperplastic":[17],"polyps":[18],"(HP,":[19],"benign":[20],"growth)":[21],"and":[22,72,85,98,134],"sessile":[23],"serrated":[24],"adenomas":[25],"(SSA,":[26],"precancerous":[27],"lesions)":[28],"is":[29],"difficult":[30],"even":[31],"for":[32],"specialized":[33],"pathologists.":[34],"Convolutional":[35],"neural":[36],"networks":[37],"(CNNs)":[38],"currently":[39],"achieve":[40],"strong":[41],"performance":[42,88,125],"binary":[44],"classification":[45],"tasks,":[46],"but":[47],"they":[48],"lack":[49],"explanatory":[50,129],"capabilities":[51],"to":[52,108],"assist":[53],"physicians":[54],"interpreting":[56],"the":[57,82,101,121,128],"images.":[58],"Multimodal":[59],"Large":[60],"Language":[61],"Models":[62],"(MLLMs),":[63],"such":[64],"as":[65],"Gemini,":[66],"offer":[67],"potential":[68],"advantages":[69],"interpretability":[71],"contextual":[73,105],"explanation.":[74],"this":[76,114],"work,":[77],"we":[78],"evaluate":[79],"Gemini-2.0-flash-lite":[80],"on":[81],"MHIST":[83],"dataset":[84],"improve":[86],"its":[87],"through":[89],"five":[90],"few-shot":[91],"in-context":[92],"learning":[93],"strategies":[94],"(including":[95],"random-k,":[96],"KNN,":[97],"KNN+distance),":[99],"exploring":[100],"impact":[102],"providing":[104],"support":[106],"Gemini.":[109],"The":[110],"results":[111,135],"show":[112],"that":[113],"multimodal":[115],"approach":[116],"enhances":[117],"diagnostic":[118],"accuracy,":[119],"narrowing":[120],"gap":[122],"CNN-level":[124],"while":[126],"retaining":[127],"power":[130],"MLLMs.":[132],"Code":[133],"are":[136],"publicly":[137],"available":[138],"at":[139],"https://github.com/jjsierramo/SIPAIM.":[140]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-12-18T00:00:00"}
