{"id":"https://openalex.org/W3110749701","doi":"https://doi.org/10.5220/0010286201680173","title":"Statistical Inference of the Inter-sample Dice Distribution for Discriminative CNN Brain Lesion Segmentation Models","display_name":"Statistical Inference of the Inter-sample Dice Distribution for Discriminative CNN Brain Lesion Segmentation Models","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3110749701","doi":"https://doi.org/10.5220/0010286201680173","mag":"3110749701"},"language":"en","primary_location":{"id":"doi:10.5220/0010286201680173","is_oa":false,"landing_page_url":"https://doi.org/10.5220/0010286201680173","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2012.02755","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5108836695","display_name":"Kevin Raina","orcid":"https://orcid.org/0000-0002-6240-9675"},"institutions":[{"id":"https://openalex.org/I153718931","display_name":"University of Ottawa","ror":"https://ror.org/03c4mmv16","country_code":"CA","type":"education","lineage":["https://openalex.org/I153718931"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Kevin Raina","raw_affiliation_strings":["Department of Mathematics and Statistics, University of Ottawa, Ontario, Canada, --- Select a Country ---"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics and Statistics, University of Ottawa, Ontario, Canada, --- Select a Country ---","institution_ids":["https://openalex.org/I153718931"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5108836695"],"corresponding_institution_ids":["https://openalex.org/I153718931"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.00287621,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"168","last_page":"173"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9998999834060669,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9993000030517578,"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"}},{"id":"https://openalex.org/T14510","display_name":"Medical Imaging and Analysis","score":0.9991999864578247,"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/discriminative-model","display_name":"Discriminative model","score":0.8787615299224854},{"id":"https://openalex.org/keywords/dice","display_name":"Dice","score":0.767076313495636},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7311219573020935},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6509516835212708},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6365483999252319},{"id":"https://openalex.org/keywords/s\u00f8rensen\u2013dice-coefficient","display_name":"S\u00f8rensen\u2013Dice coefficient","score":0.6346890330314636},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6327301263809204},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5776919722557068},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5459259748458862},{"id":"https://openalex.org/keywords/conditional-random-field","display_name":"Conditional random field","score":0.5317505598068237},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.4915240705013275},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.44896554946899414},{"id":"https://openalex.org/keywords/sampling-distribution","display_name":"Sampling distribution","score":0.4430106282234192},{"id":"https://openalex.org/keywords/conditional-probability-distribution","display_name":"Conditional probability distribution","score":0.42334139347076416},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.3566821217536926},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.25138217210769653},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.229895681142807}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8787615299224854},{"id":"https://openalex.org/C22029948","wikidata":"https://www.wikidata.org/wiki/Q45089","display_name":"Dice","level":2,"score":0.767076313495636},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7311219573020935},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6509516835212708},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6365483999252319},{"id":"https://openalex.org/C163892561","wikidata":"https://www.wikidata.org/wiki/Q2613728","display_name":"S\u00f8rensen\u2013Dice coefficient","level":4,"score":0.6346890330314636},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6327301263809204},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5776919722557068},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5459259748458862},{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.5317505598068237},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.4915240705013275},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.44896554946899414},{"id":"https://openalex.org/C167723999","wikidata":"https://www.wikidata.org/wiki/Q3773214","display_name":"Sampling distribution","level":2,"score":0.4430106282234192},{"id":"https://openalex.org/C43555835","wikidata":"https://www.wikidata.org/wiki/Q2300258","display_name":"Conditional probability distribution","level":2,"score":0.42334139347076416},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.3566821217536926},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25138217210769653},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.229895681142807},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.5220/0010286201680173","is_oa":false,"landing_page_url":"https://doi.org/10.5220/0010286201680173","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2012.02755","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2012.02755","pdf_url":"https://arxiv.org/pdf/2012.02755","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2012.02755","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2012.02755","pdf_url":"https://arxiv.org/pdf/2012.02755","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"score":0.75,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1884191083","https://openalex.org/W1901129140","https://openalex.org/W1987869189","https://openalex.org/W2301358467","https://openalex.org/W2320562195","https://openalex.org/W2346367339","https://openalex.org/W2484736472","https://openalex.org/W2611775752","https://openalex.org/W2890354008","https://openalex.org/W2900298334","https://openalex.org/W2964218260","https://openalex.org/W3012431403"],"related_works":["https://openalex.org/W4402926319","https://openalex.org/W4389060404","https://openalex.org/W2973136608","https://openalex.org/W3012828488","https://openalex.org/W4286233748","https://openalex.org/W4254054209","https://openalex.org/W4200334192","https://openalex.org/W4391935352","https://openalex.org/W2952835238","https://openalex.org/W2912850978"],"abstract_inverted_index":{"Discriminative":[0],"convolutional":[1],"neural":[2],"networks":[3],"(CNNs),":[4],"for":[5,166],"which":[6,44],"a":[7,24,46,67,105,116,140,148,163,167],"voxel-wise":[8],"conditional":[9,47],"Multinoulli":[10],"distribution":[11,48,114],"is":[12,51,101,154],"assumed,":[13],"have":[14],"performed":[15],"well":[16],"in":[17,31,43,62],"many":[18],"brain":[19],"lesion":[20],"segmentation":[21,96],"tasks.":[22],"For":[23],"trained":[25,106],"discriminative":[26,99],"CNN":[27,164],"to":[28,65,103,156,159,171],"be":[29,60],"used":[30,102],"clinical":[32],"practice,":[33,75],"the":[34,41,54,57,80,89,111,130,172,177,185,191],"patient's":[35],"radiological":[36],"features":[37],"are":[38,134],"inputted":[39],"into":[40],"model,":[42,68],"case":[45],"of":[49,56],"segmentations":[50],"produced.":[52],"Capturing":[53],"uncertainty":[55],"predictions":[58,181],"can":[59,86],"useful":[61],"deciding":[63],"whether":[64,158],"abandon":[66],"or":[69,161],"choose":[70],"amongst":[71],"competing":[72],"models.":[73],"In":[74,93],"however,":[76],"we":[77],"never":[78,87],"know":[79,88],"ground":[81],"truth":[82],"segmentation,":[83],"and":[84,136,143,184],"therefore":[85],"true":[90],"model":[91,165,178],"variance.":[92],"this":[94],"work,":[95],"sampling":[97],"on":[98,115,121,190],"CNNs":[100],"assess":[104],"model's":[107],"robustness":[108],"by":[109,128,195],"analyzing":[110],"inter-sample":[112,131],"Dice":[113,132,187],"new":[117],"patient":[118],"solely":[119],"based":[120,151],"their":[122],"magnetic":[123],"resonance":[124],"(MR)":[125],"images.":[126],"Furthermore,":[127],"demonstrating":[129],"observations":[133],"independent":[135],"identically":[137],"distributed":[138],"with":[139],"finite":[141],"mean":[142],"variance":[144],"under":[145],"certain":[146],"conditions,":[147],"rigorous":[149],"confidence":[150],"decision":[152],"rule":[153],"proposed":[155],"decide":[157],"reject":[160],"accept":[162],"particular":[168],"patient.":[169],"Applied":[170],"ISLES":[173],"2015":[174],"(SISS)":[175],"dataset,":[176],"identified":[179],"7":[180],"as":[182],"non-robust,":[183],"average":[186],"coefficient":[188],"calculated":[189],"remaining":[192],"brains":[193],"improved":[194],"12":[196],"percent.":[197]},"counts_by_year":[],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
