{"id":"https://openalex.org/W4401750774","doi":"https://doi.org/10.1109/isbi56570.2024.10635193","title":"Coca-Mil: Attention-Based Handcrafted-Deep Feature Fusion in Computational Pathology","display_name":"Coca-Mil: Attention-Based Handcrafted-Deep Feature Fusion in Computational Pathology","publication_year":2024,"publication_date":"2024-05-27","ids":{"openalex":"https://openalex.org/W4401750774","doi":"https://doi.org/10.1109/isbi56570.2024.10635193"},"language":"en","primary_location":{"id":"doi:10.1109/isbi56570.2024.10635193","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/isbi56570.2024.10635193","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Symposium on Biomedical Imaging (ISBI)","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/A5017786255","display_name":"Paras Goel","orcid":null},"institutions":[{"id":"https://openalex.org/I4210129168","display_name":"BASIS International (United States)","ror":"https://ror.org/03q4sef08","country_code":"US","type":"company","lineage":["https://openalex.org/I4210129168"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Paras Goel","raw_affiliation_strings":["BASIS Independent Silicon Valley,CA,USA"],"affiliations":[{"raw_affiliation_string":"BASIS Independent Silicon Valley,CA,USA","institution_ids":["https://openalex.org/I4210129168"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073873025","display_name":"Saarthak Kapse","orcid":"https://orcid.org/0000-0002-5426-4111"},"institutions":[{"id":"https://openalex.org/I59553526","display_name":"Stony Brook University","ror":"https://ror.org/05qghxh33","country_code":"US","type":"education","lineage":["https://openalex.org/I59553526"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Saarthak Kapse","raw_affiliation_strings":["Stony Brook University,Department of Biomedical Informatics,NY,USA"],"affiliations":[{"raw_affiliation_string":"Stony Brook University,Department of Biomedical Informatics,NY,USA","institution_ids":["https://openalex.org/I59553526"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002897830","display_name":"Pushpak Pati","orcid":"https://orcid.org/0000-0003-2174-4255"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pushpak Pati","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5036343196","display_name":"Prateek Prasanna","orcid":"https://orcid.org/0000-0002-3068-3573"},"institutions":[{"id":"https://openalex.org/I59553526","display_name":"Stony Brook University","ror":"https://ror.org/05qghxh33","country_code":"US","type":"education","lineage":["https://openalex.org/I59553526"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Prateek Prasanna","raw_affiliation_strings":["Stony Brook University,Department of Biomedical Informatics,NY,USA"],"affiliations":[{"raw_affiliation_string":"Stony Brook University,Department of Biomedical Informatics,NY,USA","institution_ids":["https://openalex.org/I59553526"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5017786255"],"corresponding_institution_ids":["https://openalex.org/I4210129168"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.11403595,"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":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9980999827384949,"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":0.9980999827384949,"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/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.9815000295639038,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9397000074386597,"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/computer-science","display_name":"Computer science","score":0.6420568823814392},{"id":"https://openalex.org/keywords/coca","display_name":"Coca","score":0.5989063382148743},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5912667512893677},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5511741042137146},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4278210997581482},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.4146941900253296},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33009928464889526},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.175194650888443}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6420568823814392},{"id":"https://openalex.org/C2781313679","wikidata":"https://www.wikidata.org/wiki/Q66793593","display_name":"Coca","level":2,"score":0.5989063382148743},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5912667512893677},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5511741042137146},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4278210997581482},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.4146941900253296},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33009928464889526},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.175194650888443},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C118552586","wikidata":"https://www.wikidata.org/wiki/Q7867","display_name":"Psychiatry","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isbi56570.2024.10635193","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/isbi56570.2024.10635193","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Symposium on Biomedical Imaging (ISBI)","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":25,"referenced_works":["https://openalex.org/W1202743199","https://openalex.org/W2108598243","https://openalex.org/W2974825848","https://openalex.org/W2989761415","https://openalex.org/W3013627541","https://openalex.org/W3094502228","https://openalex.org/W3151130473","https://openalex.org/W3159481202","https://openalex.org/W3173365702","https://openalex.org/W3176719058","https://openalex.org/W3204759238","https://openalex.org/W3211647829","https://openalex.org/W4225015753","https://openalex.org/W4225157576","https://openalex.org/W4281878278","https://openalex.org/W4288809219","https://openalex.org/W4297371220","https://openalex.org/W4297491167","https://openalex.org/W4385245566","https://openalex.org/W4386352636","https://openalex.org/W4390322800","https://openalex.org/W6739901393","https://openalex.org/W6775769912","https://openalex.org/W6784333009","https://openalex.org/W6803426362"],"related_works":["https://openalex.org/W2738862710","https://openalex.org/W2419950109","https://openalex.org/W1592835980","https://openalex.org/W4244618322","https://openalex.org/W2047241159","https://openalex.org/W2125328758","https://openalex.org/W2972037610","https://openalex.org/W2257903476","https://openalex.org/W4249972412","https://openalex.org/W4386159726"],"abstract_inverted_index":{"Whole":[0],"slide":[1],"image":[2],"(WSI)":[3],"classification":[4],"in":[5,42,129],"digital":[6],"pathology":[7],"is":[8],"a":[9,47],"challenging":[10],"weakly":[11],"supervised":[12],"task":[13],"due":[14],"to":[15,68,81,114],"the":[16,20,39,103,123,131,135],"gigapixel":[17],"scale":[18],"of":[19,112,125,134],"data.":[21],"While":[22],"handcrafted":[23,66,136],"features":[24,30,53,67,139],"bring":[25],"domain-specific":[26],"insights,":[27],"deep":[28,70,138,144],"learned":[29],"offer":[31],"superior":[32],"generalizability":[33],"and":[34,74,116,137],"performance.":[35],"Drawing":[36],"inspiration":[37],"from":[38],"attention":[40],"mechanism":[41],"transformers,":[43],"we":[44,92],"introduce":[45],"CoCa-MIL,":[46],"novel":[48],"framework":[49],"that":[50,94],"unifies":[51],"these":[52],"using":[54],"Multiple":[55],"Instance":[56],"Learning":[57],"(MIL).":[58],"CoCa-MIL":[59],"comprises":[60],"two":[61],"methods:":[62],"Co-Attention,":[63],"which":[64,76],"leverages":[65],"guide":[69],"feature-based":[71],"representation":[72],"learning,":[73],"Cross-Attention,":[75],"fuses":[77],"both":[78,95],"feature":[79],"types":[80],"harness":[82],"their":[83,119],"complementary":[84,132],"information":[85],"for":[86,140],"slide-level":[87],"tasks.":[88],"In":[89],"this":[90],"study,":[91],"show":[93],"methods":[96,128],"surpass":[97],"traditional":[98],"singlefeature-type":[99],"WSI":[100],"classification.":[101],"On":[102],"TCGA":[104],"Lung":[105],"Cancer":[106],"dataset,":[107],"they":[108],"achieve":[109],"accuracy":[110],"improvements":[111],"up":[113],"2.60%":[115],"5.21%":[117],"over":[118],"respective":[120],"baselines,":[121],"underscoring":[122],"efficacy":[124],"attention-based":[126],"fusion":[127],"exploiting":[130],"nature":[133],"enhancing":[141],"performance":[142],"beyond":[143],"learning":[145],"alone.":[146]},"counts_by_year":[],"updated_date":"2025-12-26T23:08:49.675405","created_date":"2025-10-10T00:00:00"}
