{"id":"https://openalex.org/W2973136608","doi":"https://doi.org/10.1109/bhi.2019.8834667","title":"Medical Image Segmentation with Stochastic Aggregated Loss in a Unified U-Net","display_name":"Medical Image Segmentation with Stochastic Aggregated Loss in a Unified U-Net","publication_year":2019,"publication_date":"2019-05-01","ids":{"openalex":"https://openalex.org/W2973136608","doi":"https://doi.org/10.1109/bhi.2019.8834667","mag":"2973136608"},"language":"en","primary_location":{"id":"doi:10.1109/bhi.2019.8834667","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bhi.2019.8834667","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI)","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/A5006845519","display_name":"Phi Xuan Nguyen","orcid":null},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":true,"raw_author_name":"Phi Xuan Nguyen","raw_affiliation_strings":["School of EEE, Nanyang Technological University, Singapore"],"affiliations":[{"raw_affiliation_string":"School of EEE, Nanyang Technological University, Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064390827","display_name":"Zhongkang Lu","orcid":"https://orcid.org/0000-0001-7379-3193"},"institutions":[{"id":"https://openalex.org/I3005327000","display_name":"Institute for Infocomm Research","ror":"https://ror.org/053rfa017","country_code":"SG","type":"facility","lineage":["https://openalex.org/I115228651","https://openalex.org/I3005327000","https://openalex.org/I91275662"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Zhongkang Lu","raw_affiliation_strings":["Institute for Infocomm Research, Singapore"],"affiliations":[{"raw_affiliation_string":"Institute for Infocomm Research, Singapore","institution_ids":["https://openalex.org/I3005327000"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090385470","display_name":"Weimin Huang","orcid":"https://orcid.org/0000-0001-9622-5041"},"institutions":[{"id":"https://openalex.org/I3005327000","display_name":"Institute for Infocomm Research","ror":"https://ror.org/053rfa017","country_code":"SG","type":"facility","lineage":["https://openalex.org/I115228651","https://openalex.org/I3005327000","https://openalex.org/I91275662"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Weimin Huang","raw_affiliation_strings":["Institute for Infocomm Research, Singapore"],"affiliations":[{"raw_affiliation_string":"Institute for Infocomm Research, Singapore","institution_ids":["https://openalex.org/I3005327000"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112128304","display_name":"Su Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I3005327000","display_name":"Institute for Infocomm Research","ror":"https://ror.org/053rfa017","country_code":"SG","type":"facility","lineage":["https://openalex.org/I115228651","https://openalex.org/I3005327000","https://openalex.org/I91275662"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Su Huang","raw_affiliation_strings":["Institute for Infocomm Research, Singapore"],"affiliations":[{"raw_affiliation_string":"Institute for Infocomm Research, Singapore","institution_ids":["https://openalex.org/I3005327000"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111064228","display_name":"Akie Katsuki","orcid":null},"institutions":[{"id":"https://openalex.org/I4210146103","display_name":"General Electric (Japan)","ror":"https://ror.org/03g2a6c32","country_code":"JP","type":"company","lineage":["https://openalex.org/I1332737386","https://openalex.org/I4210146103"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Akie Katsuki","raw_affiliation_strings":["GE Healthcare Japan Corporation, Japan"],"affiliations":[{"raw_affiliation_string":"GE Healthcare Japan Corporation, Japan","institution_ids":["https://openalex.org/I4210146103"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049506273","display_name":"Zhiping Lin","orcid":"https://orcid.org/0000-0002-1587-1226"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Zhiping Lin","raw_affiliation_strings":["School of EEE, Nanyang Technological University, Singapore"],"affiliations":[{"raw_affiliation_string":"School of EEE, Nanyang Technological University, Singapore","institution_ids":["https://openalex.org/I172675005"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5006845519"],"corresponding_institution_ids":["https://openalex.org/I172675005"],"apc_list":null,"apc_paid":null,"fwci":0.6073,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.72560859,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9991999864578247,"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/dice","display_name":"Dice","score":0.9616308212280273},{"id":"https://openalex.org/keywords/s\u00f8rensen\u2013dice-coefficient","display_name":"S\u00f8rensen\u2013Dice coefficient","score":0.873019814491272},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.8235222697257996},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.7766680717468262},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6873911023139954},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6632445454597473},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6035715937614441},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5766488313674927},{"id":"https://openalex.org/keywords/magnetic-resonance-imaging","display_name":"Magnetic resonance imaging","score":0.5006189346313477},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.4454084634780884},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.42195022106170654},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.41172608733177185},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4093402326107025},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3652711510658264},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3637203872203827},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3415900468826294},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1621154546737671},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.14360767602920532},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.14001524448394775},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13903632760047913}],"concepts":[{"id":"https://openalex.org/C22029948","wikidata":"https://www.wikidata.org/wiki/Q45089","display_name":"Dice","level":2,"score":0.9616308212280273},{"id":"https://openalex.org/C163892561","wikidata":"https://www.wikidata.org/wiki/Q2613728","display_name":"S\u00f8rensen\u2013Dice coefficient","level":4,"score":0.873019814491272},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.8235222697257996},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.7766680717468262},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6873911023139954},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6632445454597473},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6035715937614441},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5766488313674927},{"id":"https://openalex.org/C143409427","wikidata":"https://www.wikidata.org/wiki/Q161238","display_name":"Magnetic resonance imaging","level":2,"score":0.5006189346313477},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.4454084634780884},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.42195022106170654},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.41172608733177185},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4093402326107025},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3652711510658264},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3637203872203827},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3415900468826294},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1621154546737671},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.14360767602920532},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.14001524448394775},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13903632760047913},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bhi.2019.8834667","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bhi.2019.8834667","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6399999856948853,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1641498739","https://openalex.org/W1901129140","https://openalex.org/W1987869189","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2402302915","https://openalex.org/W2502312327","https://openalex.org/W2578877523","https://openalex.org/W2703616258","https://openalex.org/W2900237898","https://openalex.org/W2962914239","https://openalex.org/W2963150697","https://openalex.org/W2963276418","https://openalex.org/W2964121744","https://openalex.org/W4295245474","https://openalex.org/W6639824700","https://openalex.org/W6684191040","https://openalex.org/W6712909570","https://openalex.org/W6724804524","https://openalex.org/W6732751041","https://openalex.org/W6756198706"],"related_works":["https://openalex.org/W3152950745","https://openalex.org/W2973136608","https://openalex.org/W3105697449","https://openalex.org/W3135174555","https://openalex.org/W3089025284","https://openalex.org/W3164697193","https://openalex.org/W3128451644","https://openalex.org/W3106986940","https://openalex.org/W4205532977","https://openalex.org/W2920794225"],"abstract_inverted_index":{"Automatic":[0],"segmentation":[1,179],"of":[2,67,147],"medical":[3,58],"images,":[4,118],"such":[5,119],"as":[6,120],"computed":[7],"tomography":[8],"(CT)":[9],"or":[10],"magnetic":[11],"resonance":[12],"imaging":[13],"(MRI),":[14],"plays":[15],"an":[16,64],"essential":[17],"role":[18],"in":[19,29,57,97,104,116,125,156,170],"efficient":[20,65],"clinical":[21,44,105],"diagnosis.":[22],"While":[23,92],"deep":[24],"learning":[25],"have":[26,35],"gained":[27],"popularity":[28],"academia":[30],"and":[31,74,122,144],"industry,":[32],"more":[33],"works":[34],"to":[36,39,82,151],"be":[37],"done":[38],"improve":[40,152],"the":[41,68,71,75,78,93,113,117,148,166,181,187],"performance":[42],"for":[43],"practice.":[45],"U-Net":[46],"architecture,":[47],"along":[48],"with":[49,84],"Dice":[50,79,140,172],"coefficient":[51],"optimization,":[52],"has":[53],"shown":[54,115],"its":[55,153],"effectiveness":[56],"image":[59],"segmentation.":[60],"Although":[61],"it":[62,100],"is":[63,95,101],"measurement":[66],"difference":[69],"between":[70],"ground":[72],"truth":[73],"network's":[76],"output,":[77],"loss":[80,135],"struggles":[81],"train":[83],"samples":[85],"that":[86],"do":[87],"not":[88],"contain":[89],"targeted":[90],"objects.":[91],"situation":[94],"unusual":[96],"standard":[98],"datasets,":[99],"commonly":[102],"seen":[103],"data,":[106],"where":[107],"many":[108],"training":[109],"data":[110],"available":[111],"without":[112],"anomalies":[114],"lesions":[121],"anatomic":[123],"structures":[124],"some":[126],"CTs/regions.":[127],"In":[128,174],"this":[129],"paper,":[130],"we":[131],"propose":[132],"a":[133,145],"novel":[134],"function":[136],"-":[137],"Stochastic":[138],"Aggregated":[139],"Coefficient":[141],"(SA":[142],"Dice)":[143],"modification":[146],"network":[149],"structure":[150],"performance.":[154],"Experimentally,":[155],"our":[157,163],"own":[158],"heart":[159],"aorta":[160],"CT":[161],"dataset,":[162],"models":[164,182],"beats":[165],"baseline":[167],"by":[168,189],"4%":[169],"cross-validation":[171],"scores.":[173],"BRATS":[175],"2017":[176],"brain":[177],"tumor":[178],"challenge,":[180],"also":[183],"perform":[184],"better":[185],"than":[186],"state-of-the-art":[188],"approximately":[190],"2%.":[191]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}