{"id":"https://openalex.org/W3089990511","doi":"https://doi.org/10.1109/ijcnn48605.2020.9206940","title":"Learning Non-Unique Segmentation with Reward-Penalty Dice Loss","display_name":"Learning Non-Unique Segmentation with Reward-Penalty Dice Loss","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3089990511","doi":"https://doi.org/10.1109/ijcnn48605.2020.9206940","mag":"3089990511"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn48605.2020.9206940","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9206940","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","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/A5041537053","display_name":"Jiabo He","orcid":null},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Jiabo He","raw_affiliation_strings":["School of Computing and Information Systems, The University of Melbourne, Australia"],"affiliations":[{"raw_affiliation_string":"School of Computing and Information Systems, The University of Melbourne, Australia","institution_ids":["https://openalex.org/I165779595"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070030398","display_name":"Sarah Erfani","orcid":"https://orcid.org/0000-0003-0885-0643"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Sarah Erfani","raw_affiliation_strings":["School of Computing and Information Systems, The University of Melbourne, Australia"],"affiliations":[{"raw_affiliation_string":"School of Computing and Information Systems, The University of Melbourne, Australia","institution_ids":["https://openalex.org/I165779595"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054649005","display_name":"Sudanthi Wijewickrema","orcid":"https://orcid.org/0000-0001-8015-8577"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Sudanthi Wijewickrema","raw_affiliation_strings":["Department of Otolaryngology, The University of Melbourne, Australia"],"affiliations":[{"raw_affiliation_string":"Department of Otolaryngology, The University of Melbourne, Australia","institution_ids":["https://openalex.org/I165779595"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027127816","display_name":"Stephen O\u2019Leary","orcid":"https://orcid.org/0000-0001-6926-2103"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Stephen O'Leary","raw_affiliation_strings":["Department of Otolaryngology, The University of Melbourne, Australia"],"affiliations":[{"raw_affiliation_string":"Department of Otolaryngology, The University of Melbourne, Australia","institution_ids":["https://openalex.org/I165779595"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069895484","display_name":"Kotagiri Ramamohanarao","orcid":"https://orcid.org/0000-0003-3304-9268"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Kotagiri Ramamohanarao","raw_affiliation_strings":["School of Computing and Information Systems, The University of Melbourne, Australia"],"affiliations":[{"raw_affiliation_string":"School of Computing and Information Systems, The University of Melbourne, Australia","institution_ids":["https://openalex.org/I165779595"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5041537053"],"corresponding_institution_ids":["https://openalex.org/I165779595"],"apc_list":null,"apc_paid":null,"fwci":0.0977,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.40500437,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9990000128746033,"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":0.9990000128746033,"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":0.9973000288009644,"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/T14510","display_name":"Medical Imaging and Analysis","score":0.9966999888420105,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/dice","display_name":"Dice","score":0.9005493521690369},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.8719366788864136},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7802024483680725},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7143837213516235},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6085745096206665},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5816804766654968},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5628054738044739},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5121512413024902},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.48920321464538574},{"id":"https://openalex.org/keywords/segmentation-based-object-categorization","display_name":"Segmentation-based object categorization","score":0.4569675028324127},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4405099153518677},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4351046085357666},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.42386019229888916},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.34815651178359985},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07399016618728638}],"concepts":[{"id":"https://openalex.org/C22029948","wikidata":"https://www.wikidata.org/wiki/Q45089","display_name":"Dice","level":2,"score":0.9005493521690369},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.8719366788864136},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7802024483680725},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7143837213516235},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6085745096206665},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5816804766654968},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5628054738044739},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5121512413024902},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.48920321464538574},{"id":"https://openalex.org/C25694479","wikidata":"https://www.wikidata.org/wiki/Q7446278","display_name":"Segmentation-based object categorization","level":5,"score":0.4569675028324127},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4405099153518677},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4351046085357666},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.42386019229888916},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.34815651178359985},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07399016618728638},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"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/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn48605.2020.9206940","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9206940","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4699999988079071,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W2078908690","https://openalex.org/W2081385919","https://openalex.org/W2099471712","https://openalex.org/W2127890285","https://openalex.org/W2169311208","https://openalex.org/W2194775991","https://openalex.org/W2410664773","https://openalex.org/W2464708700","https://openalex.org/W2480078828","https://openalex.org/W2601686579","https://openalex.org/W2622064152","https://openalex.org/W2726555724","https://openalex.org/W2734349601","https://openalex.org/W2751909359","https://openalex.org/W2794022343","https://openalex.org/W2796762894","https://openalex.org/W2798122215","https://openalex.org/W2898091194","https://openalex.org/W2907965334","https://openalex.org/W2929753309","https://openalex.org/W2962914239","https://openalex.org/W2963046541","https://openalex.org/W2963276418","https://openalex.org/W2966975099","https://openalex.org/W2998201756","https://openalex.org/W3101639073","https://openalex.org/W4289489408","https://openalex.org/W4320013936","https://openalex.org/W6639824700","https://openalex.org/W6740861035","https://openalex.org/W6743645564","https://openalex.org/W6748542448","https://openalex.org/W6750469568","https://openalex.org/W6750599543","https://openalex.org/W6754551840","https://openalex.org/W6754606571","https://openalex.org/W6755875945","https://openalex.org/W6757855356","https://openalex.org/W6761274798","https://openalex.org/W6771686265"],"related_works":["https://openalex.org/W3144569342","https://openalex.org/W2185902295","https://openalex.org/W2945274617","https://openalex.org/W2103507220","https://openalex.org/W2055202857","https://openalex.org/W2371519352","https://openalex.org/W4205800335","https://openalex.org/W2386644571","https://openalex.org/W2551987074","https://openalex.org/W2372421320"],"abstract_inverted_index":{"Semantic":[0],"segmentation":[1,27,32,41,62,83,111],"is":[2,36,104],"one":[3,38],"of":[4,11,25,106,128],"the":[5,9,72,87,94,126],"key":[6],"problems":[7],"in":[8,52,75],"field":[10],"computer":[12,17],"vision,":[13],"as":[14,64,93],"it":[15],"enables":[16],"image":[18],"understanding.":[19],"However,":[20],"most":[21],"research":[22],"and":[23,116],"applications":[24],"semantic":[26],"focus":[28],"on":[29,140],"addressing":[30],"unique":[31],"problems,":[33,54],"where":[34],"there":[35],"only":[37],"gold":[39],"standard":[40],"result":[42],"for":[43,71,97],"every":[44],"input":[45],"image.":[46],"This":[47],"may":[48,59,67],"not":[49],"be":[50],"true":[51],"some":[53],"e.g.,":[55],"medical":[56],"applications.":[57],"We":[58],"have":[60],"non-unique":[61,82,110],"annotations":[63],"different":[65,77],"surgeons":[66],"perform":[68],"successful":[69],"surgeries":[70],"same":[73],"patient":[74],"slightly":[76],"ways.":[78],"To":[79],"comprehensively":[80],"learn":[81,109],"tasks,":[84],"we":[85],"propose":[86],"reward-penalty":[88],"Dice":[89],"loss":[90,138],"(RPDL)":[91],"function":[92],"optimization":[95],"objective":[96],"deep":[98],"convolutional":[99],"neural":[100],"networks":[101],"(DCNN).":[102],"RPDL":[103,124],"capable":[105],"helping":[107],"DCNN":[108,129],"by":[112,131],"enhancing":[113],"common":[114],"regions":[115],"penalizing":[117],"outside":[118],"ones.":[119],"Experimental":[120],"results":[121],"show":[122],"that":[123],"improves":[125],"performance":[127],"models":[130],"up":[132],"to":[133],"18.4%":[134],"compared":[135],"with":[136],"other":[137],"functions":[139],"our":[141],"collected":[142],"surgical":[143],"dataset.":[144]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}