{"id":"https://openalex.org/W3164531903","doi":"https://doi.org/10.1109/isbi48211.2021.9434108","title":"Automatic Prostate Cancer Detection On Multi-Parametric Mri With Hierarchical Weakly Supervised Learning","display_name":"Automatic Prostate Cancer Detection On Multi-Parametric Mri With Hierarchical Weakly Supervised Learning","publication_year":2021,"publication_date":"2021-04-13","ids":{"openalex":"https://openalex.org/W3164531903","doi":"https://doi.org/10.1109/isbi48211.2021.9434108","mag":"3164531903"},"language":"en","primary_location":{"id":"doi:10.1109/isbi48211.2021.9434108","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi48211.2021.9434108","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)","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/A5013868893","display_name":"Haibo Yang","orcid":"https://orcid.org/0000-0002-5715-0849"},"institutions":[{"id":"https://openalex.org/I4210135459","display_name":"United Imaging Healthcare (China)","ror":"https://ror.org/03qqw3m37","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210135459"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haibo Yang","raw_affiliation_strings":["Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China","institution_ids":["https://openalex.org/I4210135459"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020602841","display_name":"Guangyu Wu","orcid":"https://orcid.org/0000-0002-7043-2357"},"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"]},{"id":"https://openalex.org/I2800570007","display_name":"Renji Hospital","ror":"https://ror.org/03ypbx660","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I2800570007"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"GuangYu Wu","raw_affiliation_strings":["Renji Hospital, School of Medicine, Shanghai Jiao Tong University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Renji Hospital, School of Medicine, Shanghai Jiao Tong University, China","institution_ids":["https://openalex.org/I183067930","https://openalex.org/I2800570007"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000937401","display_name":"Dinggang Shen","orcid":"https://orcid.org/0000-0002-7934-5698"},"institutions":[{"id":"https://openalex.org/I4210135459","display_name":"United Imaging Healthcare (China)","ror":"https://ror.org/03qqw3m37","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210135459"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dinggang Shen","raw_affiliation_strings":["Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China","institution_ids":["https://openalex.org/I4210135459"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040212201","display_name":"Shu Liao","orcid":"https://orcid.org/0009-0002-4668-5204"},"institutions":[{"id":"https://openalex.org/I4210135459","display_name":"United Imaging Healthcare (China)","ror":"https://ror.org/03qqw3m37","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210135459"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shu Liao","raw_affiliation_strings":["Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China","institution_ids":["https://openalex.org/I4210135459"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":7.7865,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.9711603,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"316","last_page":"319"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10124","display_name":"Prostate Cancer Diagnosis and Treatment","score":0.996399998664856,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"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/T10124","display_name":"Prostate Cancer Diagnosis and Treatment","score":0.996399998664856,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9952999949455261,"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/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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7772135138511658},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7415327429771423},{"id":"https://openalex.org/keywords/workflow","display_name":"Workflow","score":0.6348170638084412},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5977727770805359},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.49909043312072754},{"id":"https://openalex.org/keywords/prostate-cancer","display_name":"Prostate cancer","score":0.49525681138038635},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4650600552558899},{"id":"https://openalex.org/keywords/computer-aided-diagnosis","display_name":"Computer-aided diagnosis","score":0.44695669412612915},{"id":"https://openalex.org/keywords/cad","display_name":"CAD","score":0.4433921277523041},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3373661935329437},{"id":"https://openalex.org/keywords/cancer","display_name":"Cancer","score":0.21584942936897278},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.09917405247688293}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7772135138511658},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7415327429771423},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.6348170638084412},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5977727770805359},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49909043312072754},{"id":"https://openalex.org/C2780192828","wikidata":"https://www.wikidata.org/wiki/Q181257","display_name":"Prostate cancer","level":3,"score":0.49525681138038635},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4650600552558899},{"id":"https://openalex.org/C2779549770","wikidata":"https://www.wikidata.org/wiki/Q1122413","display_name":"Computer-aided diagnosis","level":2,"score":0.44695669412612915},{"id":"https://openalex.org/C194789388","wikidata":"https://www.wikidata.org/wiki/Q17855283","display_name":"CAD","level":2,"score":0.4433921277523041},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3373661935329437},{"id":"https://openalex.org/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.21584942936897278},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.09917405247688293},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","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},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isbi48211.2021.9434108","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi48211.2021.9434108","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)","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":12,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2095284457","https://openalex.org/W2106033751","https://openalex.org/W2133287637","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2800850596","https://openalex.org/W2804517750","https://openalex.org/W2962914239","https://openalex.org/W2979653841","https://openalex.org/W6639824700","https://openalex.org/W6752179672"],"related_works":["https://openalex.org/W1589419489","https://openalex.org/W4229543669","https://openalex.org/W1783185948","https://openalex.org/W615772105","https://openalex.org/W2999505641","https://openalex.org/W2401866201","https://openalex.org/W2945629533","https://openalex.org/W1554029525","https://openalex.org/W2013631688","https://openalex.org/W2553942461"],"abstract_inverted_index":{"Multi-parametric":[0],"MRI":[1],"(mp-MRI)":[2],"is":[3,52,97,103],"one":[4],"of":[5,46,58,76,94,129],"the":[6,44,68,73,84,89,95,133],"most":[7],"commonly":[8],"used":[9],"non-invasive":[10],"methods":[11],"for":[12,24,43,144],"prostate":[13],"cancer":[14],"(PCa)":[15],"diagnosis.":[16],"In":[17,79],"recent":[18],"years,":[19],"computer":[20],"aided":[21],"diagnosis":[22,51],"(CAD)":[23],"PCa":[25,50,61,77,85,134,145,157],"on":[26,29,64,87,147],"mp-MRI":[27,65],"based":[28,49,123],"deep":[30,47,121],"learning":[31,48,122],"techniques":[32],"has":[33],"gained":[34],"much":[35],"attention":[36],"and":[37,159],"shown":[38],"promising":[39,156],"progress.":[40],"The":[41],"key":[42],"success":[45],"to":[53,81,106,117,125],"obtain":[54,107],"a":[55,119,138],"large":[56,74],"amount":[57],"high":[59],"quality":[60],"region":[62,86],"annotation":[63],"such":[66],"that":[67,152],"network":[69],"can":[70],"accurately":[71],"learn":[72],"variation":[75],"lesions.":[78],"order":[80],"precisely":[82],"annotate":[83],"mp-MRI,":[88],"pathological":[90],"whole":[91],"mount":[92],"data":[93],"patient":[96],"normally":[98],"required":[99],"as":[100],"reference,":[101],"which":[102],"often":[104],"difficult":[105],"in":[108,132],"real":[109],"world":[110],"clinical":[111],"situations.":[112],"Therefore,":[113],"we":[114],"are":[115],"motivated":[116],"propose":[118],"new":[120],"method":[124,154],"integrate":[126],"different":[127],"levels":[128],"information":[130],"available":[131],"screening":[135],"workflow":[136],"through":[137],"multitask":[139],"hierarchical":[140],"weakly":[141],"supervised":[142],"framework":[143],"detection":[146,158],"mp-MRI.":[148],"Experimental":[149],"results":[150],"show":[151],"our":[153],"achieves":[155],"segmentation":[160],"results.":[161]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}