{"id":"https://openalex.org/W4393213207","doi":"https://doi.org/10.48550/arxiv.2403.16335","title":"MEDDAP: Medical Dataset Enhancement via Diversified Augmentation\n Pipeline","display_name":"MEDDAP: Medical Dataset Enhancement via Diversified Augmentation\n Pipeline","publication_year":2024,"publication_date":"2024-03-24","ids":{"openalex":"https://openalex.org/W4393213207","doi":"https://doi.org/10.48550/arxiv.2403.16335"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2403.16335","pdf_url":"http://arxiv.org/pdf/2403.16335","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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},"type":"preprint","type_crossref":"posted-content","indexed_in":["arxiv"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://arxiv.org/pdf/2403.16335","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5021111864","display_name":"Yasamin Medghalchi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Medghalchi, Yasamin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079171087","display_name":"Niloufar Zakariaei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zakariaei, Niloufar","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021438906","display_name":"Arman Rahmim","orcid":"https://orcid.org/0000-0002-9980-2403"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rahmim, Arman","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5015205742","display_name":"Ilker Hacihaliloglu","orcid":"https://orcid.org/0000-0003-3232-8193"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hacihaliloglu, Ilker","raw_affiliation_strings":[],"affiliations":[]}],"institution_assertions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":0,"max":83},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9072,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9072,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[],"concepts":[{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.8034571},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.44462058},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.39277983},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3809191},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.045970798}],"mesh":[],"locations_count":1,"locations":[{"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2403.16335","pdf_url":"http://arxiv.org/pdf/2403.16335","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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false}],"best_oa_location":{"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2403.16335","pdf_url":"http://arxiv.org/pdf/2403.16335","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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4391913857","https://openalex.org/W2748952813","https://openalex.org/W2530322880","https://openalex.org/W2478288626","https://openalex.org/W2390279801","https://openalex.org/W2382290278","https://openalex.org/W2376932109","https://openalex.org/W2358668433","https://openalex.org/W2350741829","https://openalex.org/W2001405890"],"abstract_inverted_index":{"The":[0,250],"effectiveness":[1],"of":[2,14,55,75,82,163,171,181],"Deep":[3],"Neural":[4],"Networks":[5],"(DNNs)":[6],"heavily":[7],"relies":[8],"on":[9,23,72,121,237],"the":[10,48,73,79,83,178,192,198,238],"abundance":[11],"and":[12,20,31,78,124],"accuracy":[13],"available":[15,254],"training":[16,125],"data.":[17],"However,":[18],"collecting":[19],"annotating":[21],"data":[22],"a":[24,91,149,222],"large":[25],"scale":[26],"is":[27,58,208,253],"often":[28,220],"both":[29],"costly":[30],"time-intensive,":[32],"particularly":[33],"in":[34,60],"medical":[35,61,128],"cases":[36],"where":[37,217],"practitioners":[38],"are":[39,118],"already":[40],"occupied":[41],"with":[42,66],"their":[43,136],"duties.":[44],"Moreover,":[45],"ensuring":[46],"that":[47,69],"model":[49],"remains":[50],"robust":[51],"across":[52,203],"various":[53],"scenarios":[54],"image":[56],"capture":[57],"crucial":[59,224],"domains,":[62],"especially":[63],"when":[64,246],"dealing":[65],"ultrasound":[67,156],"images":[68,129],"vary":[70],"based":[71,120],"settings":[74],"different":[76,189,204],"devices":[77],"manual":[80],"operation":[81],"transducer.":[84],"To":[85,139,183],"address":[86],"this":[87,141],"challenge,":[88,142],"we":[89,143,187],"introduce":[90,144],"novel":[92,150],"pipeline":[93,231],"called":[94],"MEDDAP,":[95],"which":[96],"leverages":[97],"Stable":[98],"Diffusion":[99],"(SD)":[100],"models":[101],"to":[102,135,174,200],"augment":[103],"existing":[104],"small":[105],"datasets":[106],"by":[107,210],"automatically":[108],"generating":[109],"new":[110],"informative":[111],"labeled":[112],"samples.":[113],"Pretrained":[114],"checkpoints":[115],"for":[116,127,155,160],"SD":[117],"typically":[119],"natural":[122],"images,":[123],"them":[126],"requires":[130],"significant":[131],"GPU":[132],"resources":[133],"due":[134],"heavy":[137],"parameters.":[138],"overcome":[140],"USLoRA":[145,158],"(Ultrasound":[146],"Low-Rank":[147],"Adaptation),":[148],"fine-tuning":[151,162,176],"method":[152],"tailored":[153],"specifically":[154],"applications.":[157],"allows":[159],"selective":[161],"weights":[164],"within":[165],"SD,":[166],"requiring":[167],"fewer":[168],"than":[169,226],"0.1\\%":[170],"parameters":[172],"compared":[173],"fully":[175],"only":[177,233],"UNet":[179],"portion":[180],"SD.":[182],"enhance":[184],"dataset":[185,240],"diversity,":[186],"incorporate":[188],"adjectives":[190],"into":[191],"generation":[193],"process":[194],"prompts,":[195],"thereby":[196],"desensitizing":[197],"classifiers":[199,235],"intensity":[201],"changes":[202],"images.":[205],"This":[206],"approach":[207],"inspired":[209],"clinicians'":[211],"decision-making":[212],"processes":[213],"regarding":[214],"breast":[215],"tumors,":[216],"tumor":[218],"shape":[219],"plays":[221],"more":[223],"role":[225],"intensity.":[227],"In":[228],"conclusion,":[229],"our":[230],"not":[232],"outperforms":[234],"trained":[236],"original":[239],"but":[241],"also":[242],"demonstrates":[243],"superior":[244],"performance":[245],"encountering":[247],"unseen":[248],"datasets.":[249],"source":[251],"code":[252],"at":[255],"https://github.com/yasamin-med/MEDDAP.":[256]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4393213207","counts_by_year":[],"updated_date":"2025-01-07T02:58:20.085423","created_date":"2024-03-27"}