{"id":"https://openalex.org/W2168817733","doi":"https://doi.org/10.1109/mlsp.2011.6064554","title":"Lesion detection of gastroscopic images based on cost-sensitive boosting","display_name":"Lesion detection of gastroscopic images based on cost-sensitive boosting","publication_year":2011,"publication_date":"2011-09-01","ids":{"openalex":"https://openalex.org/W2168817733","doi":"https://doi.org/10.1109/mlsp.2011.6064554","mag":"2168817733"},"language":"en","primary_location":{"id":"doi:10.1109/mlsp.2011.6064554","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp.2011.6064554","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2011 IEEE International Workshop on Machine Learning for Signal Processing","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/A5100302934","display_name":"Kai Sun","orcid":"https://orcid.org/0009-0006-1633-2941"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Kai Sun","raw_affiliation_strings":["School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055402306","display_name":"Shuheng Zhang","orcid":"https://orcid.org/0000-0001-5277-249X"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuheng Zhang","raw_affiliation_strings":["School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064877153","display_name":"Rui Yao","orcid":"https://orcid.org/0000-0002-3415-9859"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rui Yao","raw_affiliation_strings":["School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044127793","display_name":"Wei Yang","orcid":"https://orcid.org/0000-0002-2161-3231"},"institutions":[{"id":"https://openalex.org/I58200834","display_name":"Southern Medical University","ror":"https://ror.org/01vjw4z39","country_code":"CN","type":"education","lineage":["https://openalex.org/I58200834"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Yang","raw_affiliation_strings":["School of Biomedical Engineering, Southern Medical University, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Biomedical Engineering, Southern Medical University, Guangzhou, China","institution_ids":["https://openalex.org/I58200834"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110947469","display_name":"Shidan Cheng","orcid":null},"institutions":[{"id":"https://openalex.org/I2801556517","display_name":"Ruijin Hospital","ror":"https://ror.org/01hv94n30","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I2801556517"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shidan Cheng","raw_affiliation_strings":["Department of Gastroenterology, School of Medicine, Ruijin Hospital, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Department of Gastroenterology, School of Medicine, Ruijin Hospital, Shanghai, China","institution_ids":["https://openalex.org/I2801556517"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100716679","display_name":"Su Zhang","orcid":"https://orcid.org/0000-0001-6395-2429"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Su Zhang","raw_affiliation_strings":["School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100302934"],"corresponding_institution_ids":["https://openalex.org/I183067930"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.19170406,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"26","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9900000095367432,"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":0.9900000095367432,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9854999780654907,"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":0.982699990272522,"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/boosting","display_name":"Boosting (machine learning)","score":0.9311349391937256},{"id":"https://openalex.org/keywords/adaboost","display_name":"AdaBoost","score":0.8619197010993958},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7186568975448608},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6424520015716553},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5949137210845947},{"id":"https://openalex.org/keywords/false-positive-rate","display_name":"False positive rate","score":0.5013949871063232},{"id":"https://openalex.org/keywords/lesion","display_name":"Lesion","score":0.5011286735534668},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.34311753511428833},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.1952718198299408},{"id":"https://openalex.org/keywords/pathology","display_name":"Pathology","score":0.11276459693908691},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.08444568514823914}],"concepts":[{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.9311349391937256},{"id":"https://openalex.org/C141404830","wikidata":"https://www.wikidata.org/wiki/Q2823869","display_name":"AdaBoost","level":3,"score":0.8619197010993958},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7186568975448608},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6424520015716553},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5949137210845947},{"id":"https://openalex.org/C95922358","wikidata":"https://www.wikidata.org/wiki/Q5432725","display_name":"False positive rate","level":2,"score":0.5013949871063232},{"id":"https://openalex.org/C2781156865","wikidata":"https://www.wikidata.org/wiki/Q827023","display_name":"Lesion","level":2,"score":0.5011286735534668},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.34311753511428833},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.1952718198299408},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.11276459693908691},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.08444568514823914}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mlsp.2011.6064554","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp.2011.6064554","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2011 IEEE International Workshop on Machine Learning for Signal Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5400000214576721,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1554944419","https://openalex.org/W1556311489","https://openalex.org/W1580049798","https://openalex.org/W1588282782","https://openalex.org/W2000005775","https://openalex.org/W2024046085","https://openalex.org/W2051209726","https://openalex.org/W2067885219","https://openalex.org/W2090398718","https://openalex.org/W2104955141","https://openalex.org/W2106787323","https://openalex.org/W2107742354","https://openalex.org/W2120396754","https://openalex.org/W2134672608","https://openalex.org/W2142503713","https://openalex.org/W2146302903","https://openalex.org/W2163352848","https://openalex.org/W2171033594","https://openalex.org/W2244158382","https://openalex.org/W2373301426","https://openalex.org/W2912934387","https://openalex.org/W3124955340","https://openalex.org/W4212883601","https://openalex.org/W4248437541","https://openalex.org/W4285719527","https://openalex.org/W6634569365","https://openalex.org/W6650413072","https://openalex.org/W6675785006","https://openalex.org/W6676029316","https://openalex.org/W6830460197","https://openalex.org/W7015672083"],"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":{"Gastroscopy":[0],"is":[1,11,63,75,98],"widely":[2],"used":[3],"for":[4,65,109],"clinical":[5],"examination":[6],"of":[7,13,53,56,82,87,112,118,132],"gastric":[8],"cancer":[9],"which":[10,32],"one":[12],"the":[14,51,79,88,107,121,142],"most":[15],"serious":[16],"diseases.":[17],"Computer-aided":[18],"detection":[19,55,111,130,135],"can":[20,140],"help":[21],"physicians":[22],"to":[23,27,77],"identify":[24],"suspicious":[25],"regions":[26],"reduce":[28],"false":[29,38],"negative":[30],"diagnosis":[31],"costs":[33],"too":[34],"much":[35],"more":[36,145],"than":[37,149],"positive":[39],"diagnosis.":[40],"Three":[41],"cost-sensitive":[42,138],"boosting":[43,67],"algorithms":[44],"are":[45],"compared":[46],"in":[47,50],"this":[48],"paper":[49],"task":[52],"lesion":[54,110,143],"gastroscopic":[57,113],"images.":[58],"The":[59],"optimal":[60],"cost":[61],"structure":[62],"selected":[64],"each":[66],"algorithm.":[68],"Threshold":[69],"obtained":[70,123],"adaptively":[71],"from":[72],"training":[73,125],"set":[74,126],"adopted":[76],"get":[78],"final":[80],"result":[81],"a":[83,116,128],"novel":[84],"sample":[85],"instead":[86],"sign":[89],"function.":[90],"Classification":[91],"performance":[92],"becomes":[93],"better":[94],"after":[95],"adaptive":[96],"threshold":[97,122],"used.":[99],"Experimental":[100],"results":[101],"show":[102],"that":[103],"Cost-sensitive":[104],"AdaBoost":[105,139,150],"performs":[106],"best":[108],"images":[114],"achieving":[115],"sensitivity":[117],"77.34%":[119],"with":[120],"on":[124,137],"at":[127],"target":[129],"rate":[131],"80%.":[133],"Lesion":[134],"based":[136],"outline":[141],"area":[144],"completely":[146],"and":[147],"accurately":[148],"method.":[151]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}