{"id":"https://openalex.org/W7082974961","doi":"https://doi.org/10.5281/zenodo.14898708","title":"MedNF","display_name":"MedNF","publication_year":2025,"publication_date":"2025-09-18","ids":{"openalex":"https://openalex.org/W7082974961","doi":"https://doi.org/10.5281/zenodo.14898708"},"language":"en","primary_location":{"id":"doi:10.5281/zenodo.14898708","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.14898708","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Dataset"},"type":"dataset","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.5281/zenodo.14898708","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Friedrich, Paul","orcid":"https://orcid.org/0000-0003-3653-5624"},"institutions":[{"id":"https://openalex.org/I1850255","display_name":"University of Basel","ror":"https://ror.org/02s6k3f65","country_code":"CH","type":"education","lineage":["https://openalex.org/I1850255"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Friedrich, Paul","raw_affiliation_strings":["University of Basel"],"raw_orcid":"https://orcid.org/0000-0003-3653-5624","affiliations":[{"raw_affiliation_string":"University of Basel","institution_ids":["https://openalex.org/I1850255"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Bieder, Florentin","orcid":"https://orcid.org/0000-0001-9558-0623"},"institutions":[{"id":"https://openalex.org/I1850255","display_name":"University of Basel","ror":"https://ror.org/02s6k3f65","country_code":"CH","type":"education","lineage":["https://openalex.org/I1850255"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Bieder, Florentin","raw_affiliation_strings":["University of Basel"],"raw_orcid":"https://orcid.org/0000-0001-9558-0623","affiliations":[{"raw_affiliation_string":"University of Basel","institution_ids":["https://openalex.org/I1850255"]}]},{"author_position":"middle","author":{"id":null,"display_name":"McGinnis, Julian","orcid":"https://orcid.org/0009-0000-2224-7600"},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"McGinnis, Julian","raw_affiliation_strings":["Technical University of Munich"],"raw_orcid":"https://orcid.org/0009-0000-2224-7600","affiliations":[{"raw_affiliation_string":"Technical University of Munich","institution_ids":["https://openalex.org/I62916508"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Wolleb, Julia","orcid":"https://orcid.org/0000-0003-4087-5920"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wolleb, Julia","raw_affiliation_strings":["Yale University"],"raw_orcid":"https://orcid.org/0000-0003-4087-5920","affiliations":[{"raw_affiliation_string":"Yale University","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Rueckert, Daniel","orcid":"https://orcid.org/0000-0002-5683-5889"},"institutions":[{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"education","lineage":["https://openalex.org/I47508984"]},{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE","GB"],"is_corresponding":false,"raw_author_name":"Rueckert, Daniel","raw_affiliation_strings":["Technical University of Munich","Imperial College London"],"raw_orcid":"https://orcid.org/0000-0002-5683-5889","affiliations":[{"raw_affiliation_string":"Technical University of Munich","institution_ids":["https://openalex.org/I62916508"]},{"raw_affiliation_string":"Imperial College London","institution_ids":["https://openalex.org/I47508984"]}]},{"author_position":"last","author":{"id":null,"display_name":"Cattin, Philippe C.","orcid":"https://orcid.org/0000-0001-8785-2713"},"institutions":[{"id":"https://openalex.org/I1850255","display_name":"University of Basel","ror":"https://ror.org/02s6k3f65","country_code":"CH","type":"education","lineage":["https://openalex.org/I1850255"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Cattin, Philippe C.","raw_affiliation_strings":["University of Basel"],"raw_orcid":"https://orcid.org/0000-0001-8785-2713","affiliations":[{"raw_affiliation_string":"University of Basel","institution_ids":["https://openalex.org/I1850255"]}]}],"institutions":[],"countries_distinct_count":4,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"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":true,"primary_topic":{"id":"https://openalex.org/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.3779999911785126,"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/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.3779999911785126,"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/T13177","display_name":"Geological and Geophysical Studies","score":0.05299999937415123,"subfield":{"id":"https://openalex.org/subfields/1907","display_name":"Geology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13067","display_name":"Geological Modeling and Analysis","score":0.027899999171495438,"subfield":{"id":"https://openalex.org/subfields/1906","display_name":"Geochemistry and Petrology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6305000185966492},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.6028000116348267},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5033000111579895},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.47290000319480896},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.43779999017715454},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.3659000098705292},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.3190999925136566},{"id":"https://openalex.org/keywords/grid","display_name":"Grid","score":0.3174000084400177}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7189000248908997},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6474999785423279},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6305000185966492},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6028000116348267},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5210999846458435},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5033000111579895},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.47290000319480896},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.43779999017715454},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.3659000098705292},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3190999925136566},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.3174000084400177},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.314300000667572},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.31130000948905945},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.3077999949455261},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.3019999861717224},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2912999987602234},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.26669999957084656},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.2522999942302704},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.25200000405311584}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.5281/zenodo.14898708","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.14898708","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Dataset"}],"best_oa_location":{"id":"doi:10.5281/zenodo.14898708","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.14898708","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Dataset"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"MedNF":[0,259,315],"Dataset":[1,279],"from":[2,301],"the":[3,17,68,73,254,302,306,350,353,371,400],"Paper:":[4],"\"MedFuncta:":[5,22],"A":[6,23],"Unified":[7,24],"Framework":[8,25],"for":[9,26,116,195,267],"Learning":[10,27],"Efficient":[11,28],"Medical":[12,29],"Neural":[13,30,76],"Fields\"":[14,31],"This":[15],"is":[16,436],"official":[18],"dataset,":[19,311],"realeased":[20],"with":[21,61,289,318],"by":[32,83],"Paul":[33],"Friedrich,":[34],"Florentin":[35],"Bieder,":[36],"Julian":[37],"McGinnis,":[38],"Julia":[39],"Wolleb,":[40],"Daniel":[41],"Rueckert":[42],"and":[43,64,167,174,209,227,253,352,402,425],"Philippe":[44],"C.":[45],"Cattin.":[46],"Abstract":[47],"Research":[48],"in":[49,96,162,184,244,345,359,370],"medical":[50,97,103,122,225,269],"imaging":[51],"primarily":[52],"focuses":[53],"on":[54,120,125,235,349],"discrete":[55],"data":[56,85,130,238,411],"representations":[57,298],"that":[58,140,199],"poorly":[59],"scale":[60],"grid":[62],"resolution":[63],"fail":[65],"to":[66,101,181,230,275,331],"capture":[67],"often":[69],"continuous":[70,87],"nature":[71],"of":[72,224,285,309,320],"underlying":[74],"signal.":[75,328],"Fields":[77],"(NFs)":[78],"offer":[79],"a":[80,113,132,136,142,149,157,169,191,221,283,326,430,439],"powerful":[81],"alternative":[82],"modeling":[84],"as":[86,325,409],"functions.":[88],"While":[89],"single-instance":[90],"NFs":[91],"have":[92],"successfully":[93],"been":[94],"applied":[95],"contexts,":[98],"extending":[99],"them":[100],"large-scale":[102,117,256],"datasets":[104,226,351,369],"remains":[105],"an":[106],"open":[107],"challenge.":[108],"We":[109,151,188,281,329,366],"therefore":[110],"introduce":[111,190],"MedFuncta,":[112],"unified":[114,133],"framework":[115],"NF":[118],"training":[119],"diverse":[121,222],"signals.":[123],"Building":[124],"Functa,":[126],"our":[127,179,236,249,360],"approach":[128],"encodes":[129],"into":[131],"representation,":[134],"namely":[135],"1D":[137],"latent":[138,265],"vector,":[139],"modulates":[141],"shared,":[143],"meta-learned":[144],"NF,":[145],"enabling":[146],"generalization":[147],"across":[148,220],"dataset.":[150,304],"revisit":[152],"common":[153],"design":[154],"choices,":[155],"introducing":[156],"non-constant":[158],"frequency":[159],"parameter":[160],"$\\omega$":[161],"widely":[163],"used":[164],"SIREN":[165],"activations,":[166],"establish":[168],"connection":[170],"between":[171],"this":[172,245,310],"$\\omega$-schedule":[173],"layer-wise":[175],"learning":[176,186,198],"rates,":[177],"relating":[178],"findings":[180],"recent":[182],"work":[183],"theoretical":[185],"dynamics.":[187],"additionally":[189],"scalable":[192],"meta-learning":[193],"strategy":[194],"shared":[196],"network":[197],"employs":[200],"sparse":[201],"supervision":[202,327,334],"during":[203],"training,":[204],"thereby":[205],"reducing":[206],"memory":[207],"consumption":[208],"computational":[210],"overhead":[211],"while":[212],"maintaining":[213],"competitive":[214],"performance.":[215],"Finally,":[216],"we":[217,247,312],"evaluate":[218],"MedFuncta":[219,287],"range":[223],"show":[228],"how":[229],"solve":[231],"relevant":[232],"downstream":[233],"tasks":[234],"neural":[237,294],"representation.":[239],"To":[240],"promote":[241],"further":[242],"research":[243],"direction,":[246],"release":[248,282,367],"code,":[250],"model":[251],"weights":[252],"first":[255,307],"dataset":[257],"-":[258,260],"containing":[261],">":[262,290],"500":[263],"k":[264,292],"vectors":[266],"multi-instance":[268],"NFs.":[270],"Project":[271],"page":[272],"|":[273,277],"Link":[274],"arXiv":[276],"Code":[278],"Descriptor":[280],"total":[284],"7":[286,314],"sets,":[288],"550":[291],"annotated":[293],"fields.":[295],"The":[296],"released":[297,437],"are":[299,427],"derived":[300],"MedMNIST":[303],"In":[305],"version":[308],"publish":[313],"sets":[316],"trained":[317],"images":[319],"64":[321,323],"x":[322,337,340,343],"pixels":[324],"plan":[330],"add":[332],"other":[333],"resolutions":[335],"(28":[336],"28,":[338],"128":[339],"128,":[341],"224":[342],"224)":[344],"future":[346],"releases.":[347],"Information":[348],"according":[354,403],"labels":[355],"can":[356,406],"be":[357,407],"found":[358],"paper":[361],"(Appendix":[362],"D).":[363],"Data":[364],"structure":[365],"all":[368],"following":[372],"form:":[373],"\u2514\u2500\u2500\u2500PneumoniaNF":[374],"\u2514\u2500\u2500\u2500test":[375,391],"\u2514\u2500\u2500\u2500datapoint_0.pt":[376,381,386],"\u2514\u2500\u2500\u2500datapoint_1.pt":[377,382,387],"\u2514\u2500\u2500\u2500datapoint_2.pt":[378,383,388],"...":[379,384,389,392,394],"\u2514\u2500\u2500\u2500train":[380,393],"\u2514\u2500\u2500\u2500val":[385,395],"\u2514\u2500\u2500\u2500model.pt\u2514\u2500\u2500\u2500RetinaNF":[390],"......":[396],"Each":[397],"datapoint.pt":[398],"contains":[399],"representation":[401],"labels.":[404],"They":[405],"accessed":[408],"follows:":[410],"=":[412,416],"torch.load(self.files[idx],":[413],"weights_only=False)modulations,":[414],"label":[415],"data['modulations'].float(),":[417],"data['label']":[418],"License":[419],"ChestNF,":[420],"PathNF,":[421],"OctNF,":[422],"PneumoniaNF,":[423],"RetinaNF":[424],"TissueNF":[426],"relesed":[428],"under":[429,438],"CC":[431,440],"BY":[432],"4.0":[433,442],"License.":[434,443],"DermaNF":[435],"BY-NC":[441]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2025-10-10T00:00:00"}