{"id":"https://openalex.org/W4401750168","doi":"https://doi.org/10.1109/isbi56570.2024.10635522","title":"Self-Supervised Learning of Dense Hierarchical Representations for Medical Image Segmentation","display_name":"Self-Supervised Learning of Dense Hierarchical Representations for Medical Image Segmentation","publication_year":2024,"publication_date":"2024-05-27","ids":{"openalex":"https://openalex.org/W4401750168","doi":"https://doi.org/10.1109/isbi56570.2024.10635522"},"language":"en","primary_location":{"id":"doi:10.1109/isbi56570.2024.10635522","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/isbi56570.2024.10635522","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Symposium on Biomedical Imaging (ISBI)","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/A5011329007","display_name":"Eytan Kats","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Eytan Kats","raw_affiliation_strings":["University of Lübeck,Institute of Medical Informatics,Germany"],"affiliations":[{"raw_affiliation_string":"University of Lübeck,Institute of Medical Informatics,Germany","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103525913","display_name":"Jochen G. Hirsch","orcid":null},"institutions":[{"id":"https://openalex.org/I4210137873","display_name":"Fraunhofer Institute for Digital Medicine","ror":"https://ror.org/04farme71","country_code":"DE","type":"facility","lineage":["https://openalex.org/I4210137873","https://openalex.org/I4923324"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Jochen G. Hirsch","raw_affiliation_strings":["Fraunhofer Institute for Digital Medicine MEVIS,Bremen,Germany"],"affiliations":[{"raw_affiliation_string":"Fraunhofer Institute for Digital Medicine MEVIS,Bremen,Germany","institution_ids":["https://openalex.org/I4210137873"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064304390","display_name":"Mattias P. Heinrich\u202c","orcid":"https://orcid.org/0000-0002-7489-1972"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mattias P. Heinrich","raw_affiliation_strings":["University of Lübeck,Institute of Medical Informatics,Germany"],"affiliations":[{"raw_affiliation_string":"University of Lübeck,Institute of Medical Informatics,Germany","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5011329007"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.115183,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"35","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.8730999827384949,"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"}},"topics":[{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.8730999827384949,"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.7461000084877014,"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":0.7250999808311462,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7254290580749512},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.695112943649292},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6839942336082458},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6372858881950378},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.5098316073417664},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.49460718035697937},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4876590967178345},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4418545663356781},{"id":"https://openalex.org/keywords/segmentation-based-object-categorization","display_name":"Segmentation-based object categorization","score":0.4277097284793854},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.4170493483543396},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.1667538285255432}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7254290580749512},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.695112943649292},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6839942336082458},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6372858881950378},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.5098316073417664},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49460718035697937},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4876590967178345},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4418545663356781},{"id":"https://openalex.org/C25694479","wikidata":"https://www.wikidata.org/wiki/Q7446278","display_name":"Segmentation-based object categorization","level":5,"score":0.4277097284793854},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.4170493483543396},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.1667538285255432}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/isbi56570.2024.10635522","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/isbi56570.2024.10635522","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Symposium on Biomedical Imaging (ISBI)","raw_type":"proceedings-article"},{"id":"pmh:oai:publica.fraunhofer.de:publica/510312","is_oa":false,"landing_page_url":"https://publica.fraunhofer.de/handle/publica/510312","pdf_url":null,"source":{"id":"https://openalex.org/S4306400318","display_name":"Fraunhofer-Publica (Fraunhofer-Gesellschaft)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4923324","host_organization_name":"Fraunhofer-Gesellschaft","host_organization_lineage":["https://openalex.org/I4923324"],"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":"conference paper"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W2979708377","https://openalex.org/W3168822201","https://openalex.org/W3203435379","https://openalex.org/W4224237482","https://openalex.org/W4225927986","https://openalex.org/W4283072464","https://openalex.org/W4294605033","https://openalex.org/W4307246905","https://openalex.org/W4307972630","https://openalex.org/W4312428231","https://openalex.org/W4312742492","https://openalex.org/W4313156423","https://openalex.org/W4319300504","https://openalex.org/W4387211540","https://openalex.org/W4390871833","https://openalex.org/W6774314701","https://openalex.org/W6839656003","https://openalex.org/W6902762063"],"related_works":["https://openalex.org/W3144569342","https://openalex.org/W2945274617","https://openalex.org/W1986655823","https://openalex.org/W2185902295","https://openalex.org/W2103507220","https://openalex.org/W2055202857","https://openalex.org/W2371519352","https://openalex.org/W4205800335","https://openalex.org/W2386644571","https://openalex.org/W2372421320"],"abstract_inverted_index":{"This":[0],"paper":[1],"demonstrates":[2],"a":[3,48,81,87],"self-supervised":[4],"framework":[5],"for":[6,12,25,109],"learning":[7,28],"voxel-wise":[8],"coarse-to-fine":[9],"representations":[10,64],"tailored":[11],"dense":[13],"downstream":[14],"tasks.":[15],"Our":[16,71,125],"approach":[17,106],"stems":[18],"from":[19,57],"the":[20,53,62,118],"observation":[21],"that":[22,51,61,103],"existing":[23],"methods":[24],"hierarchical":[26],"representation":[27],"tend":[29],"to":[30,38],"prioritize":[31],"global":[32],"features":[33,36,56],"over":[34],"local":[35,77],"due":[37],"inherent":[39],"architectural":[40],"bias.":[41],"To":[42],"address":[43],"this":[44],"challenge,":[45],"we":[46],"devise":[47],"training":[49],"strategy":[50,72],"balances":[52],"contributions":[54],"of":[55],"multiple":[58],"scales,":[59],"ensuring":[60],"learned":[63],"capture":[65],"both":[66],"coarse":[67],"and":[68,85,98,101,115,127],"fine-grained":[69],"details.":[70],"incorporates":[73],"3-fold":[74],"improvements:":[75],"(1)":[76],"data":[78,100,114],"augmentations,":[79],"(2)":[80],"hierarchically":[82],"balanced":[83],"architecture,":[84],"(3)":[86],"hybrid":[88],"contrastive-restorative":[89],"loss":[90],"function.":[91],"We":[92],"evaluate":[93],"our":[94,104],"method":[95],"on":[96],"CT":[97],"MRI":[99],"demonstrate":[102],"new":[105],"particularly":[107],"beneficial":[108],"fine-tuning":[110],"with":[111],"limited":[112],"annotated":[113],"consistently":[116],"outperforms":[117],"baseline":[119],"counterpart":[120],"in":[121],"linear":[122],"evaluation":[123],"settings.":[124],"code":[126],"pre-trained":[128],"models":[129],"will":[130],"be":[131],"publicly":[132],"available":[133],"at":[134],"https://github.com/multimodallearning/hierarchical-dense-ssl.":[135]},"counts_by_year":[],"updated_date":"2026-03-18T14:38:29.013473","created_date":"2025-10-10T00:00:00"}