{"id":"https://openalex.org/W2150487955","doi":"https://doi.org/10.1109/cvpr.2009.5206778","title":"A min-max framework of cascaded classifier with multiple instance learning for computer aided diagnosis","display_name":"A min-max framework of cascaded classifier with multiple instance learning for computer aided diagnosis","publication_year":2009,"publication_date":"2009-06-01","ids":{"openalex":"https://openalex.org/W2150487955","doi":"https://doi.org/10.1109/cvpr.2009.5206778","mag":"2150487955"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2009.5206778","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2009.5206778","pdf_url":null,"source":{"id":"https://openalex.org/S4363607795","display_name":"2009 IEEE Conference on Computer Vision and Pattern Recognition","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 IEEE Conference on Computer Vision and Pattern Recognition","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/A5074384370","display_name":"Dijia Wu","orcid":"https://orcid.org/0000-0002-0636-9912"},"institutions":[{"id":"https://openalex.org/I165799507","display_name":"Rensselaer Polytechnic Institute","ror":"https://ror.org/01rtyzb94","country_code":"US","type":"education","lineage":["https://openalex.org/I165799507"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Dijia Wu","raw_affiliation_strings":["Rensselaer Polytechnic Institute, Troy, NY, USA"],"affiliations":[{"raw_affiliation_string":"Rensselaer Polytechnic Institute, Troy, NY, USA","institution_ids":["https://openalex.org/I165799507"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051393532","display_name":"Jinbo Bi","orcid":"https://orcid.org/0000-0001-6996-4092"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jinbo Bi","raw_affiliation_strings":["Siemens Medical Solutions, Inc., Malvern, PA, USA"],"affiliations":[{"raw_affiliation_string":"Siemens Medical Solutions, Inc., Malvern, PA, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103528488","display_name":"Kim L. Boyer","orcid":null},"institutions":[{"id":"https://openalex.org/I165799507","display_name":"Rensselaer Polytechnic Institute","ror":"https://ror.org/01rtyzb94","country_code":"US","type":"education","lineage":["https://openalex.org/I165799507"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kim Boyer","raw_affiliation_strings":["Rensselaer Polytechnic Institute, Troy, NY, USA"],"affiliations":[{"raw_affiliation_string":"Rensselaer Polytechnic Institute, Troy, NY, USA","institution_ids":["https://openalex.org/I165799507"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5074384370"],"corresponding_institution_ids":["https://openalex.org/I165799507"],"apc_list":null,"apc_paid":null,"fwci":0.6221,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.74087373,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1359","last_page":"1366"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9990000128746033,"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"}},"topics":[{"id":"https://openalex.org/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9990000128746033,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9574999809265137,"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/T10552","display_name":"Colorectal Cancer Screening and Detection","score":0.9225000143051147,"subfield":{"id":"https://openalex.org/subfields/2730","display_name":"Oncology"},"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":0.6543790102005005},{"id":"https://openalex.org/keywords/cad","display_name":"CAD","score":0.626880407333374},{"id":"https://openalex.org/keywords/quadratic-growth","display_name":"Quadratic growth","score":0.605774462223053},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5394229888916016},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5334076881408691},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5287327766418457},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.48317399621009827},{"id":"https://openalex.org/keywords/quadratic-equation","display_name":"Quadratic equation","score":0.4792447090148926},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.47174108028411865},{"id":"https://openalex.org/keywords/quadratic-programming","display_name":"Quadratic programming","score":0.46052393317222595},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.41977524757385254},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4116475582122803},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39759689569473267},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38609641790390015},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2767407298088074},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.21813490986824036},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16492637991905212}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6543790102005005},{"id":"https://openalex.org/C194789388","wikidata":"https://www.wikidata.org/wiki/Q17855283","display_name":"CAD","level":2,"score":0.626880407333374},{"id":"https://openalex.org/C195956108","wikidata":"https://www.wikidata.org/wiki/Q7268362","display_name":"Quadratic growth","level":2,"score":0.605774462223053},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5394229888916016},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5334076881408691},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5287327766418457},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.48317399621009827},{"id":"https://openalex.org/C129844170","wikidata":"https://www.wikidata.org/wiki/Q41299","display_name":"Quadratic equation","level":2,"score":0.4792447090148926},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.47174108028411865},{"id":"https://openalex.org/C81845259","wikidata":"https://www.wikidata.org/wiki/Q290117","display_name":"Quadratic programming","level":2,"score":0.46052393317222595},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.41977524757385254},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4116475582122803},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39759689569473267},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38609641790390015},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2767407298088074},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.21813490986824036},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16492637991905212},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C199639397","wikidata":"https://www.wikidata.org/wiki/Q1788588","display_name":"Engineering drawing","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/cvpr.2009.5206778","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2009.5206778","pdf_url":null,"source":{"id":"https://openalex.org/S4363607795","display_name":"2009 IEEE Conference on Computer Vision and Pattern Recognition","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 IEEE Conference on Computer Vision and Pattern Recognition","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.156.8487","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.156.8487","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.rpi.edu/~bij2/doc/milcas_cvpr09.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.6200000047683716,"display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1540764732","https://openalex.org/W1966233349","https://openalex.org/W2058680827","https://openalex.org/W2107143555","https://openalex.org/W2108745803","https://openalex.org/W2110119381","https://openalex.org/W2125508569","https://openalex.org/W2128678390","https://openalex.org/W2135046866","https://openalex.org/W2135607475","https://openalex.org/W2147474239","https://openalex.org/W2148469355","https://openalex.org/W2154318594","https://openalex.org/W2157176527","https://openalex.org/W2163474322","https://openalex.org/W2166010828","https://openalex.org/W2170110077","https://openalex.org/W3097096317","https://openalex.org/W6676205484","https://openalex.org/W6676245398","https://openalex.org/W6679227803","https://openalex.org/W6683033130"],"related_works":["https://openalex.org/W2112847829","https://openalex.org/W4293088966","https://openalex.org/W2074925460","https://openalex.org/W1878408459","https://openalex.org/W4297407962","https://openalex.org/W2753327353","https://openalex.org/W4297797735","https://openalex.org/W2917463375","https://openalex.org/W2108152589","https://openalex.org/W1496670269"],"abstract_inverted_index":{"The":[0],"computer":[1],"aided":[2],"diagnosis":[3],"(CAD)":[4],"problems":[5,112],"of":[6,33,113],"detecting":[7,114],"potentially":[8],"diseased":[9],"structures":[10],"from":[11,120],"medical":[12],"images":[13],"are":[14,46],"typically":[15],"distinguished":[16],"by":[17,100],"the":[18,41,106,110,132,141,156],"following":[19],"challenging":[20],"characteristics:":[21],"extremely":[22],"unbalanced":[23],"data":[24,166,172],"between":[25],"negative":[26],"and":[27,49,70,97,117,170],"positive":[28,37],"classes;":[29],"stringent":[30],"real-time":[31],"requirement":[32],"online":[34],"execution;":[35],"multiple":[36,71],"candidates":[38],"generated":[39],"for":[40,152],"same":[42],"malignant":[43],"structure":[44],"that":[45,127],"highly":[47],"correlated":[48],"spatially":[50],"close":[51],"to":[52,66,81,90,109,140],"each":[53],"other.":[54],"To":[55],"address":[56],"all":[57],"these":[58],"problems,":[59,155],"we":[60],"propose":[61],"a":[62,76,82,91],"novel":[63],"learning":[64,73],"formulation":[65],"combine":[67],"cascade":[68],"classification":[69],"instance":[72],"(MIL)":[74],"in":[75],"unified":[77],"min-max":[78],"framework,":[79],"leading":[80],"joint":[83],"optimization":[84,102],"problem":[85],"which":[86],"can":[87],"be":[88],"converted":[89],"tractable":[92],"quadratically":[93],"constrained":[94],"quadratic":[95],"program":[96],"efficiently":[98],"solved":[99],"block-coordinate":[101],"algorithms.":[103],"We":[104],"apply":[105],"proposed":[107,157],"approach":[108,129],"CAD":[111],"pulmonary":[115],"embolism":[116],"colon":[118],"cancer":[119],"computed":[121],"tomography":[122],"images.":[123],"Experimental":[124],"results":[125],"show":[126],"our":[128],"significantly":[130],"reduces":[131],"computational":[133],"cost":[134],"while":[135],"yielding":[136],"comparable":[137],"detection":[138],"accuracy":[139],"current":[142],"state-of-the-art":[143],"MIL":[144,154,164],"or":[145],"cascaded":[146],"classifiers.":[147],"Although":[148],"not":[149],"specifically":[150],"designed":[151],"balanced":[153,163],"method":[158],"achieves":[159],"superior":[160],"performance":[161],"on":[162],"benchmark":[165],"such":[167],"as":[168],"MUSK":[169],"image":[171],"sets.":[173]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":2},{"year":2015,"cited_by_count":4},{"year":2014,"cited_by_count":2},{"year":2012,"cited_by_count":4}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}