{"id":"https://openalex.org/W2961533511","doi":"https://doi.org/10.1109/isbi.2019.8759148","title":"What And How Other Datasets Can Be Leveraged For Medical Imaging Classification","display_name":"What And How Other Datasets Can Be Leveraged For Medical Imaging Classification","publication_year":2019,"publication_date":"2019-04-01","ids":{"openalex":"https://openalex.org/W2961533511","doi":"https://doi.org/10.1109/isbi.2019.8759148","mag":"2961533511"},"language":"en","primary_location":{"id":"doi:10.1109/isbi.2019.8759148","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi.2019.8759148","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5044780027","display_name":"Hong Shang","orcid":"https://orcid.org/0000-0001-5333-8943"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hong Shang","raw_affiliation_strings":["Tencent AI Lab, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent AI Lab, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038814111","display_name":"Zhongqian Sun","orcid":null},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhongqian Sun","raw_affiliation_strings":["Tencent AI Lab, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent AI Lab, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072354726","display_name":"Xinghui Fu","orcid":"https://orcid.org/0000-0002-7143-0185"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinghui Fu","raw_affiliation_strings":["Tencent AI Lab, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent AI Lab, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100337459","display_name":"Zijian Zhang","orcid":"https://orcid.org/0000-0002-2702-8025"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zijian Zhang","raw_affiliation_strings":["Tencent AI Lab, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent AI Lab, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044127793","display_name":"Wei Yang","orcid":"https://orcid.org/0000-0002-2161-3231"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Yang","raw_affiliation_strings":["Tencent AI Lab, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent AI Lab, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I2250653659"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"35","issue":null,"first_page":"814","last_page":"818"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10552","display_name":"Colorectal Cancer Screening and Detection","score":0.9966999888420105,"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"}},"topics":[{"id":"https://openalex.org/T10552","display_name":"Colorectal Cancer Screening and Detection","score":0.9966999888420105,"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"}},{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9950000047683716,"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":0.9889000058174133,"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":0.8240535259246826},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7419251203536987},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6789566874504089},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.6343930959701538},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6093109846115112},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5641148686408997},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5019631385803223},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.49698761105537415},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.48581767082214355},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.47821757197380066},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.46756941080093384},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4369071125984192},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.41265496611595154}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8240535259246826},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7419251203536987},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6789566874504089},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.6343930959701538},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6093109846115112},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5641148686408997},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5019631385803223},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.49698761105537415},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.48581767082214355},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.47821757197380066},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.46756941080093384},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4369071125984192},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.41265496611595154},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isbi.2019.8759148","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi.2019.8759148","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)","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":20,"referenced_works":["https://openalex.org/W1536680647","https://openalex.org/W1896424170","https://openalex.org/W2010122281","https://openalex.org/W2117539524","https://openalex.org/W2156749117","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2253429366","https://openalex.org/W2296073425","https://openalex.org/W2346062110","https://openalex.org/W2794825826","https://openalex.org/W2913340405","https://openalex.org/W2963446712","https://openalex.org/W2963877604","https://openalex.org/W2963946669","https://openalex.org/W3102785203","https://openalex.org/W6631782140","https://openalex.org/W6639495226","https://openalex.org/W6682764186","https://openalex.org/W6684191040"],"related_works":["https://openalex.org/W2361861616","https://openalex.org/W2263699433","https://openalex.org/W3204418343","https://openalex.org/W4292388283","https://openalex.org/W1560624709","https://openalex.org/W2576964996","https://openalex.org/W3214142563","https://openalex.org/W3166286441","https://openalex.org/W4221162086","https://openalex.org/W3111760155"],"abstract_inverted_index":{"Applying":[0],"deep":[1],"learning":[2,80],"for":[3,13],"medical":[4],"imaging":[5,60,121],"classification":[6],"suffers":[7],"from":[8,95,144],"limited":[9],"labeled":[10],"data,":[11],"especially":[12],"those":[14],"relying":[15],"on":[16,34,104,155],"biopsy":[17],"proven":[18],"labels.":[19],"In":[20],"this":[21,25],"work":[22],"we":[23],"approach":[24],"challenge":[26],"by":[27,101],"leveraging":[28],"other":[29,149],"available":[30],"datasets":[31,68],"with":[32,65,69,82,136,153,157],"focus":[33],"answering":[35],"what":[36],"dataset":[37,110,141],"can":[38],"be":[39],"used,":[40],"and":[41,72,99,139],"how":[42],"to":[43,129,151],"use":[44],"them":[45],"optimally.":[46],"An":[47,108],"application":[48],"was":[49],"considered,":[50],"classifying":[51],"benign":[52],"or":[53,123],"malignant":[54],"colonic":[55],"polyps":[56],"given":[57],"colonoscopy":[58],"narrow-band":[59],"(NBI).":[61],"Experiments":[62],"were":[63],"conducted":[64],"5":[66,158],"auxiliary":[67,109,140],"various":[70],"characteristics,":[71],"multiple":[73],"training":[74],"schemes":[75],"involving":[76],"transfer":[77],"learning,":[78],"multitask":[79],"(MTL)":[81],"a":[83],"DenseNet":[84],"backbone.":[85],"Results":[86],"consistently":[87],"suggest":[88],"an":[89],"optimal":[90],"scheme":[91,138],"of":[92,120],"MTL":[93],"initialized":[94],"ImageNet":[96],"pretrain":[97],"model":[98],"followed":[100],"fine":[102],"tuning":[103],"the":[105],"main":[106],"task.":[107],"helps":[111],"better":[112],"if":[113],"it's":[114],"not":[115],"only":[116],"similar":[117],"in":[118],"terms":[119],"mechanism":[122],"fine-grained":[124],"variability,":[125],"but":[126],"also":[127],"easy":[128],"train":[130],"itself.":[131],"A":[132],"NBI":[133],"classifier":[134],"trained":[135],"such":[137],"improved":[142],"accuracy":[143],"62.6%":[145],"(baseline":[146],"without":[147],"using":[148],"dataset)":[150],"70.7%":[152],"performance":[154],"par":[156],"human":[159],"experts.":[160]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}