{"id":"https://openalex.org/W2007396059","doi":"https://doi.org/10.1109/isbi.2013.6556451","title":"Anatomical landmark detection using multiple instance boosting with spatial regularization","display_name":"Anatomical landmark detection using multiple instance boosting with spatial regularization","publication_year":2013,"publication_date":"2013-04-01","ids":{"openalex":"https://openalex.org/W2007396059","doi":"https://doi.org/10.1109/isbi.2013.6556451","mag":"2007396059"},"language":"en","primary_location":{"id":"doi:10.1109/isbi.2013.6556451","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi.2013.6556451","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE 10th International Symposium on Biomedical Imaging","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/A5070897658","display_name":"Paul Swoboda","orcid":"https://orcid.org/0000-0003-4065-8758"},"institutions":[{"id":"https://openalex.org/I223822909","display_name":"Heidelberg University","ror":"https://ror.org/038t36y30","country_code":"DE","type":"education","lineage":["https://openalex.org/I223822909"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Paul Swoboda","raw_affiliation_strings":["University of Heidelberg, Heidelberg, Germany","Univ. of Heidelberg, Heidelberg, Germany"],"affiliations":[{"raw_affiliation_string":"University of Heidelberg, Heidelberg, Germany","institution_ids":["https://openalex.org/I223822909"]},{"raw_affiliation_string":"Univ. of Heidelberg, Heidelberg, Germany","institution_ids":["https://openalex.org/I223822909"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100739507","display_name":"David Liu","orcid":"https://orcid.org/0000-0001-6116-658X"},"institutions":[{"id":"https://openalex.org/I4210137693","display_name":"Siemens (United States)","ror":"https://ror.org/04axb7e79","country_code":"US","type":"company","lineage":["https://openalex.org/I1325886976","https://openalex.org/I4210137693"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"David Liu","raw_affiliation_strings":["Siemens Corporation, Corporate Technology, Princeton, NJ, USA","Corp. Technol., Siemens Corp., Princeton, NJ, USA"],"affiliations":[{"raw_affiliation_string":"Siemens Corporation, Corporate Technology, Princeton, NJ, USA","institution_ids":["https://openalex.org/I4210137693"]},{"raw_affiliation_string":"Corp. Technol., Siemens Corp., Princeton, NJ, USA","institution_ids":["https://openalex.org/I4210137693"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028465673","display_name":"S. Kevin Zhou","orcid":"https://orcid.org/0000-0002-6881-4444"},"institutions":[{"id":"https://openalex.org/I4210137693","display_name":"Siemens (United States)","ror":"https://ror.org/04axb7e79","country_code":"US","type":"company","lineage":["https://openalex.org/I1325886976","https://openalex.org/I4210137693"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"S. Kevin Zhou","raw_affiliation_strings":["Siemens Corporation, Corporate Technology, Princeton, NJ, USA","Corp. Technol., Siemens Corp., Princeton, NJ, USA"],"affiliations":[{"raw_affiliation_string":"Siemens Corporation, Corporate Technology, Princeton, NJ, USA","institution_ids":["https://openalex.org/I4210137693"]},{"raw_affiliation_string":"Corp. Technol., Siemens Corp., Princeton, NJ, USA","institution_ids":["https://openalex.org/I4210137693"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5070897658"],"corresponding_institution_ids":["https://openalex.org/I223822909"],"apc_list":null,"apc_paid":null,"fwci":0.3997,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.63457176,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"3954","issue":null,"first_page":"218","last_page":"221"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14510","display_name":"Medical Imaging and Analysis","score":0.9890999794006348,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T14510","display_name":"Medical Imaging and Analysis","score":0.9890999794006348,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.983299970626831,"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/T12549","display_name":"Image and Object Detection Techniques","score":0.9821000099182129,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.8180782794952393},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.747018575668335},{"id":"https://openalex.org/keywords/landmark","display_name":"Landmark","score":0.7097552418708801},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6683725118637085},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6602844595909119},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.646921694278717},{"id":"https://openalex.org/keywords/false-positive-paradox","display_name":"False positive paradox","score":0.5671557784080505},{"id":"https://openalex.org/keywords/adaboost","display_name":"AdaBoost","score":0.5640759468078613},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5490515232086182},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.4969692528247833},{"id":"https://openalex.org/keywords/grid","display_name":"Grid","score":0.43604952096939087},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36847206950187683},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3205539584159851},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1906130313873291},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.14743778109550476}],"concepts":[{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.8180782794952393},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.747018575668335},{"id":"https://openalex.org/C2780297707","wikidata":"https://www.wikidata.org/wiki/Q4895393","display_name":"Landmark","level":2,"score":0.7097552418708801},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6683725118637085},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6602844595909119},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.646921694278717},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.5671557784080505},{"id":"https://openalex.org/C141404830","wikidata":"https://www.wikidata.org/wiki/Q2823869","display_name":"AdaBoost","level":3,"score":0.5640759468078613},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5490515232086182},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.4969692528247833},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.43604952096939087},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36847206950187683},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3205539584159851},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1906130313873291},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.14743778109550476},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isbi.2013.6556451","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi.2013.6556451","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE 10th International Symposium on Biomedical Imaging","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6000000238418579,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1988790447","https://openalex.org/W2024046085","https://openalex.org/W2060945009","https://openalex.org/W2107580859","https://openalex.org/W2108263314","https://openalex.org/W2113295315","https://openalex.org/W2115240329","https://openalex.org/W2135502357","https://openalex.org/W2144197083","https://openalex.org/W2146514558","https://openalex.org/W2166010828","https://openalex.org/W2167089254","https://openalex.org/W4231340930","https://openalex.org/W6676249281","https://openalex.org/W6676909469","https://openalex.org/W6677492792","https://openalex.org/W6680075925","https://openalex.org/W6681241391","https://openalex.org/W6681670809","https://openalex.org/W6684274140"],"related_works":["https://openalex.org/W2327035729","https://openalex.org/W2348748958","https://openalex.org/W3039673966","https://openalex.org/W1538046993","https://openalex.org/W2884325279","https://openalex.org/W1570592793","https://openalex.org/W1525436954","https://openalex.org/W2385662756","https://openalex.org/W2585372724","https://openalex.org/W2241444561"],"abstract_inverted_index":{"We":[0],"propose":[1],"a":[2,82,107,115],"novel":[3],"multiple":[4],"instance":[5],"boosting":[6],"approach":[7,145],"with":[8,93,100,129,159],"spatial":[9,78],"regularization":[10],"for":[11,40,74],"detecting":[12,156],"anatomical":[13,157],"landmark":[14],"to":[15,22,72,113],"alleviate":[16],"the":[17,33,43,61,77,86,90,122,143],"manual":[18],"annotation":[19],"burden":[20],"and":[21,64],"address":[23],"imprecise":[24],"annotations.":[25,164],"It":[26],"features":[27],"three":[28],"contributions.":[29],"The":[30,69,110,132],"first":[31],"is":[32,71,97,112],"introduction":[34],"of":[35,89],"soft":[36],"max":[37],"cost":[38],"function":[39],"better":[41,75,148],"handling":[42],"practical":[44],"situation":[45],"in":[46,81,98,106,155],"object":[47],"detection":[48,76,117,124,149,153],"that":[49,103,121],"most":[50],"positive":[51],"bags":[52],"only":[53],"contain":[54],"very":[55],"few":[56,160],"true":[57],"positives":[58],"while":[59],"including":[60],"ISR":[62],"rule":[63],"AdaBoost":[65],"as":[66],"special":[67],"examples.":[68],"second":[70],"exploit":[73],"context":[79],"embedded":[80],"medical":[83],"image,":[84],"specifically":[85],"grid":[87],"arrangement":[88],"training":[91],"instances":[92,105],"strong":[94],"correlation.":[95],"This":[96],"contrast":[99],"conventional":[101],"methods":[102,154],"treat":[104],"bag":[108],"independently.":[109],"third":[111],"encourage":[114],"concentrated":[116],"response":[118],"map":[119],"so":[120],"final":[123],"result":[125],"can":[126],"be":[127],"derived":[128],"more":[130],"confidence.":[131],"latter":[133],"two":[134],"contributions":[135],"are":[136],"realized":[137],"using":[138],"total":[139],"variation":[140],"regularization.":[141],"Experimentally":[142],"proposed":[144],"achieves":[146],"significantly":[147],"performance":[150],"than":[151],"state-of-the-art":[152],"landmarks":[158],"or":[161],"even":[162],"no":[163]},"counts_by_year":[{"year":2016,"cited_by_count":1},{"year":2013,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}