{"id":"https://openalex.org/W4320036811","doi":"https://doi.org/10.1007/978-3-031-25082-8_31","title":"Self-supervised Pretraining for\u00a02D Medical Image Segmentation","display_name":"Self-supervised Pretraining for\u00a02D Medical Image Segmentation","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4320036811","doi":"https://doi.org/10.1007/978-3-031-25082-8_31"},"language":"en","primary_location":{"id":"doi:10.1007/978-3-031-25082-8_31","is_oa":true,"landing_page_url":"https://doi.org/10.1007/978-3-031-25082-8_31","pdf_url":"https://link.springer.com/content/pdf/10.1007/978-3-031-25082-8_31.pdf","source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"book series"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lecture Notes in Computer Science","raw_type":"book-chapter"},"type":"book-chapter","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/978-3-031-25082-8_31.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5003922822","display_name":"Andr\u00e1s Kalapos","orcid":"https://orcid.org/0000-0002-9018-1372"},"institutions":[{"id":"https://openalex.org/I29770179","display_name":"Budapest University of Technology and Economics","ror":"https://ror.org/02w42ss30","country_code":"HU","type":"education","lineage":["https://openalex.org/I29770179"]}],"countries":["HU"],"is_corresponding":true,"raw_author_name":"Andr\u00e1s Kalapos","raw_affiliation_strings":["Department of Telecommunications and Media Informatics, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, M\u0171egyetem rkp. 3., Budapest, 1111, Hungary"],"raw_orcid":"https://orcid.org/0000-0002-9018-1372","affiliations":[{"raw_affiliation_string":"Department of Telecommunications and Media Informatics, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, M\u0171egyetem rkp. 3., Budapest, 1111, Hungary","institution_ids":["https://openalex.org/I29770179"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018247113","display_name":"B\u00e1lint Gyires-T\u00f3th","orcid":"https://orcid.org/0000-0003-1059-9822"},"institutions":[{"id":"https://openalex.org/I29770179","display_name":"Budapest University of Technology and Economics","ror":"https://ror.org/02w42ss30","country_code":"HU","type":"education","lineage":["https://openalex.org/I29770179"]}],"countries":["HU"],"is_corresponding":false,"raw_author_name":"B\u00e1lint Gyires-T\u00f3th","raw_affiliation_strings":["Department of Telecommunications and Media Informatics, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, M\u0171egyetem rkp. 3., Budapest, 1111, Hungary"],"raw_orcid":"https://orcid.org/0000-0003-1059-9822","affiliations":[{"raw_affiliation_string":"Department of Telecommunications and Media Informatics, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, M\u0171egyetem rkp. 3., Budapest, 1111, Hungary","institution_ids":["https://openalex.org/I29770179"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5003922822"],"corresponding_institution_ids":["https://openalex.org/I29770179"],"apc_list":{"value":5000,"currency":"EUR","value_usd":5392},"apc_paid":{"value":5000,"currency":"EUR","value_usd":5392},"fwci":11.0373,"has_fulltext":true,"cited_by_count":28,"citation_normalized_percentile":{"value":0.98925801,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"472","last_page":"484"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9993000030517578,"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":0.9993000030517578,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9990000128746033,"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"}},{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9937999844551086,"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/computer-science","display_name":"Computer science","score":0.825092077255249},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7472500801086426},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6980201601982117},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5798872113227844},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.5320034623146057},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5162608623504639},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.49857020378112793},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.49348533153533936},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4686521887779236},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4530297815799713},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.4484805166721344},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.4451524019241333},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.41757065057754517},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.41476574540138245},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.1868835985660553}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.825092077255249},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7472500801086426},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6980201601982117},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5798872113227844},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.5320034623146057},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5162608623504639},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.49857020378112793},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.49348533153533936},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4686521887779236},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4530297815799713},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.4484805166721344},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.4451524019241333},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.41757065057754517},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.41476574540138245},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.1868835985660553},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","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},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/978-3-031-25082-8_31","is_oa":true,"landing_page_url":"https://doi.org/10.1007/978-3-031-25082-8_31","pdf_url":"https://link.springer.com/content/pdf/10.1007/978-3-031-25082-8_31.pdf","source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"book series"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lecture Notes in Computer Science","raw_type":"book-chapter"}],"best_oa_location":{"id":"doi:10.1007/978-3-031-25082-8_31","is_oa":true,"landing_page_url":"https://doi.org/10.1007/978-3-031-25082-8_31","pdf_url":"https://link.springer.com/content/pdf/10.1007/978-3-031-25082-8_31.pdf","source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"book series"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lecture Notes in Computer Science","raw_type":"book-chapter"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320309480","display_name":"Nvidia","ror":"https://ror.org/03jdj4y14"},{"id":"https://openalex.org/F4320311358","display_name":"Budapesti M\u0171szaki \u00e9s Gazdas\u00e1gtudom\u00e1nyi Egyetem","ror":"https://ror.org/02w42ss30"},{"id":"https://openalex.org/F4320328991","display_name":"Innov\u00e1ci\u00f3s \u00e9s Technol\u00f3giai Miniszt\u00e9rium","ror":"https://ror.org/02ey3ab96"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4320036811.pdf","grobid_xml":"https://content.openalex.org/works/W4320036811.grobid-xml"},"referenced_works_count":19,"referenced_works":["https://openalex.org/W343636949","https://openalex.org/W1677182931","https://openalex.org/W1901129140","https://openalex.org/W2025768430","https://openalex.org/W2082704080","https://openalex.org/W2108598243","https://openalex.org/W2194775991","https://openalex.org/W2321533354","https://openalex.org/W2326925005","https://openalex.org/W2775461895","https://openalex.org/W2804047627","https://openalex.org/W2944828972","https://openalex.org/W2963341956","https://openalex.org/W3020996329","https://openalex.org/W3035060554","https://openalex.org/W3035524453","https://openalex.org/W3120430728","https://openalex.org/W3136987292","https://openalex.org/W6931259419"],"related_works":["https://openalex.org/W4312414840","https://openalex.org/W34092691","https://openalex.org/W2794908468","https://openalex.org/W2531570999","https://openalex.org/W2943467239","https://openalex.org/W1571801203","https://openalex.org/W4206276646","https://openalex.org/W101422005","https://openalex.org/W192740413","https://openalex.org/W3004135598"],"abstract_inverted_index":{"Supervised":[0],"machine":[1],"learning":[2,38,95,106],"provides":[3],"state-of-the-art":[4],"solutions":[5],"to":[6,42,68,111,155,182,203,232,235],"a":[7,40,54,78],"wide":[8],"range":[9],"of":[10,25,126,198],"computer":[11],"vision":[12],"problems.":[13],"However,":[14],"the":[15,23,44,124,156,167,208,223],"need":[16,45],"for":[17,46,53,71],"copious":[18],"labelled":[19,63,89],"training":[20],"data":[21,49,64,82,141,202],"limits":[22],"capabilities":[24],"these":[26],"algorithms":[27,96,107],"in":[28,100,207,216],"scenarios":[29],"where":[30,80],"such":[31,205],"input":[32],"is":[33,77,91],"scarce":[34],"or":[35],"expensive.":[36],"Self-supervised":[37],"offers":[39],"way":[41],"lower":[43],"manually":[47],"annotated":[48,230],"by":[50],"pretraining":[51,130,147,173,199,221],"models":[52,70],"specific":[55],"domain":[56],"on":[57,132,138,148,166,200],"unlabelled":[58],"data.":[59],"In":[60,117,163],"this":[61,101,118,172,191],"approach,":[62],"are":[65,108],"solely":[66],"required":[67],"fine-tune":[69],"downstream":[72,133,161,209],"tasks.":[73],"Medical":[74],"image":[75,114,135],"segmentation":[76,115,170],"field":[79],"labelling":[81],"requires":[83,193],"expert":[84],"knowledge":[85],"and":[86,122,128,140,151,158],"collecting":[87],"large":[88],"datasets":[90],"challenging;":[92],"therefore,":[93],"self-supervised":[94,105,129,146],"promise":[97],"substantial":[98],"improvements":[99],"field.":[102],"Despite":[103],"this,":[104],"used":[109],"rarely":[110],"pretrain":[112],"medical":[113,134],"networks.":[116],"paper,":[119],"we":[120,213],"elaborate":[121],"analyse":[123],"effectiveness":[125],"supervised":[127,219],"approaches":[131],"segmentation,":[136],"focusing":[137],"convergence":[139,180,210],"efficiency.":[142],"We":[143,187],"find":[144,214],"that":[145,190],"natural":[149],"images":[150,153],"target-domain-specific":[152],"leads":[154],"fastest":[157],"most":[159],"stable":[160],"convergence.":[162],"our":[164],"experiments":[165],"ACDC":[168],"cardiac":[169],"dataset,":[171],"approach":[174,192],"achieves":[175,222],"4\u20135":[176],"times":[177],"faster":[178],"fine-tuning":[179],"compared":[181],"an":[183],"ImageNet":[184,220],"pretrained":[185],"model.":[186],"also":[188],"show":[189],"less":[194,227],"than":[195,228],"five":[196],"epochs":[197],"domain-specific":[201],"achieve":[204],"improvement":[206],"time.":[211],"Finally,":[212],"that,":[215],"low-data":[217],"scenarios,":[218],"best":[224],"accuracy,":[225],"requiring":[226],"100":[229],"samples":[231],"realise":[233],"close":[234],"minimal":[236],"error.":[237]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":6}],"updated_date":"2026-03-12T06:13:28.667946","created_date":"2025-10-10T00:00:00"}