{"id":"https://openalex.org/W4319452656","doi":"https://doi.org/10.48550/arxiv.2302.02289","title":"Selecting the Best Optimizers for Deep Learning based Medical Image Segmentation","display_name":"Selecting the Best Optimizers for Deep Learning based Medical Image Segmentation","publication_year":2023,"publication_date":"2023-02-05","ids":{"openalex":"https://openalex.org/W4319452656","doi":"https://doi.org/10.48550/arxiv.2302.02289"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2302.02289","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2302.02289","pdf_url":"https://arxiv.org/pdf/2302.02289","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":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2302.02289","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5032322712","display_name":"Aliasghar Mortazi","orcid":"https://orcid.org/0000-0001-5909-3166"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Mortazi, Aliasghar","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023724443","display_name":"Vedat \u00c7i\u00e7ek","orcid":"https://orcid.org/0000-0002-3763-0570"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cicek, Vedat","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072616384","display_name":"Elif Kele\u015f","orcid":"https://orcid.org/0000-0001-8103-797X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Keles, Elif","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5030188696","display_name":"Ula\u015f Ba\u011fc\u0131","orcid":"https://orcid.org/0000-0001-7379-6829"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bagci, Ulas","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5032322712"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9708999991416931,"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":0.9708999991416931,"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/T11801","display_name":"Reservoir Engineering and Simulation Methods","score":0.90829998254776,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/segmentation","display_name":"Segmentation","score":0.7969942092895508},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6715176701545715},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6683536767959595},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6507954597473145},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5284224152565002},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4719730317592621},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4640687108039856},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.46083006262779236},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.43031519651412964},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.4230876564979553},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38024598360061646}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7969942092895508},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6715176701545715},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6683536767959595},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6507954597473145},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5284224152565002},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4719730317592621},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4640687108039856},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.46083006262779236},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.43031519651412964},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.4230876564979553},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38024598360061646},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2302.02289","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2302.02289","pdf_url":"https://arxiv.org/pdf/2302.02289","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":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2302.02289","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2302.02289","pdf_url":null,"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2302.02289","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2302.02289","pdf_url":"https://arxiv.org/pdf/2302.02289","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":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4319452656.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W2611989081","https://openalex.org/W1522196789","https://openalex.org/W3024479225","https://openalex.org/W3171371563","https://openalex.org/W2995680918","https://openalex.org/W4379141755","https://openalex.org/W4367060753","https://openalex.org/W3133954817","https://openalex.org/W3003847115"],"abstract_inverted_index":{"The":[0,259],"goal":[1],"of":[2,17,77,97,117,127,135,139,159,218],"this":[3,90,235],"work":[4],"is":[5],"to":[6,22,27,167,245,267],"identify":[7],"the":[8,15,64,70,94,110,125,151,156,180,191,247],"best":[9],"optimizers":[10,54,82,196],"for":[11,170],"deep":[12,198,253],"learning":[13,36,46,108,118,199,254],"in":[14,87,89,100,190,197,207,229,252,265],"context":[16],"cardiac":[18,140,152,171],"image":[19,137,172,231,257],"segmentation":[20,29,138,173,212,232],"and":[21,49,80,113,120,129,144,162,210,220,249],"provide":[23],"guidance":[24],"on":[25],"how":[26],"design":[28],"networks":[30,62],"with":[31,38,43,201],"effective":[32,57],"optimization":[33,222,242],"strategies.":[34],"Adaptive":[35],"helps":[37],"fast":[39],"convergence":[40],"by":[41],"starting":[42],"a":[44,101,187,226,239],"larger":[45],"rate":[47,85,119],"(LR)":[48],"gradually":[50],"decreasing":[51],"it.":[52],"Momentum":[53],"are":[55],"particularly":[56],"at":[58],"quickly":[59],"optimizing":[60],"neural":[61],"within":[63],"accelerated":[65,219],"schemes":[66],"category.":[67],"By":[68],"revealing":[69],"potential":[71],"interplay":[72],"between":[73],"these":[74],"two":[75,95],"types":[76],"algorithms":[78,99],"(LR":[79],"momentum":[81,84,121],"or":[83,203],"(MR)":[86],"short),":[88],"article,":[91],"we":[92,237],"explore":[93],"variants":[96],"SGD":[98],"single":[102,209],"setting.":[103],"We":[104,123,147,214],"suggest":[105],"using":[106,150],"cyclic":[107,241],"as":[109],"base":[111],"optimizer":[112,182],"integrating":[114],"optimal":[115],"values":[116],"rate.":[122],"investigated":[124],"relationship":[126],"LR":[128],"MR":[130],"under":[131],"an":[132],"important":[133],"problem":[134],"medical":[136,230,256],"structures":[141],"from":[142,155],"MRI":[143],"CT":[145],"scans.":[146],"conducted":[148],"experiments":[149],"imaging":[153],"dataset":[154],"ACDC":[157],"challenge":[158],"MICCAI":[160],"2017,":[161],"four":[163],"different":[164],"architectures":[165],"shown":[166],"be":[168],"successful":[169],"problems.":[174],"Our":[175],"comprehensive":[176],"evaluations":[177],"demonstrated":[178],"that":[179,216],"proposed":[181,238,260],"achieved":[183],"better":[184,263],"results":[185],"(over":[186],"2\\%":[188],"improvement":[189],"dice":[192],"metric)":[193],"than":[194],"other":[195],"literature":[200],"similar":[202],"lower":[204],"computational":[205],"cost":[206],"both":[208],"multi-object":[211],"settings.":[213],"hypothesized":[215],"combination":[217],"adaptive":[221,268],"methods":[223],"can":[224],"have":[225],"drastic":[227],"effect":[228],"performances.":[233],"To":[234],"end,":[236],"new":[240],"method":[243],"(\\textit{CLMR})":[244],"address":[246],"efficiency":[248],"accuracy":[250],"problems":[251],"based":[255],"segmentation.":[258],"strategy":[261],"yielded":[262],"generalization":[264],"comparison":[266],"optimizers.":[269]},"counts_by_year":[],"updated_date":"2026-03-11T14:59:36.786465","created_date":"2025-10-10T00:00:00"}