{"id":"https://openalex.org/W2344987838","doi":"https://doi.org/10.1109/ijcnn.2016.7727561","title":"Binary codes for tagging x-ray images via deep de-noising autoencoders","display_name":"Binary codes for tagging x-ray images via deep de-noising autoencoders","publication_year":2016,"publication_date":"2016-07-01","ids":{"openalex":"https://openalex.org/W2344987838","doi":"https://doi.org/10.1109/ijcnn.2016.7727561","mag":"2344987838"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2016.7727561","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2016.7727561","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1604.07060","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5049126089","display_name":"Antonio Sze-To","orcid":"https://orcid.org/0000-0002-0362-3516"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Antonio Sze-To","raw_affiliation_strings":["Systems Design Engineering, University of Waterloo, Waterloo, Ontario, Canada","Systems Design Engineering, University of Waterloo, Ontario, Canada N2L 3G1"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Systems Design Engineering, University of Waterloo, Waterloo, Ontario, Canada","institution_ids":["https://openalex.org/I151746483"]},{"raw_affiliation_string":"Systems Design Engineering, University of Waterloo, Ontario, Canada N2L 3G1","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038566332","display_name":"Hamid R. Tizhoosh","orcid":"https://orcid.org/0000-0001-5488-601X"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"H.R. Tizhoosh","raw_affiliation_strings":["KIMIA Lab, University of Waterloo, Waterloo, Ontario, Canada","KIMIA Lab, University of Waterloo, Ontario, Canada N2L 3G1"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"KIMIA Lab, University of Waterloo, Waterloo, Ontario, Canada","institution_ids":["https://openalex.org/I151746483"]},{"raw_affiliation_string":"KIMIA Lab, University of Waterloo, Ontario, Canada N2L 3G1","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5051841038","display_name":"Andrew K. C. Wong","orcid":"https://orcid.org/0000-0002-0019-7152"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Andrew K.C. Wong","raw_affiliation_strings":["Systems Design Engineering, University of Waterloo, Waterloo, Ontario, Canada","Systems Design Engineering, University of Waterloo, Ontario, Canada N2L 3G1"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Systems Design Engineering, University of Waterloo, Waterloo, Ontario, Canada","institution_ids":["https://openalex.org/I151746483"]},{"raw_affiliation_string":"Systems Design Engineering, University of Waterloo, Ontario, Canada N2L 3G1","institution_ids":["https://openalex.org/I151746483"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.1831,"has_fulltext":true,"cited_by_count":9,"citation_normalized_percentile":{"value":0.84899947,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9998999834060669,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9998999834060669,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9998999834060669,"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":0.9941999912261963,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7712219953536987},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.7504345178604126},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7029165029525757},{"id":"https://openalex.org/keywords/binary-code","display_name":"Binary code","score":0.6540495157241821},{"id":"https://openalex.org/keywords/hash-function","display_name":"Hash function","score":0.6331263184547424},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5729171633720398},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.557922899723053},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5512340068817139},{"id":"https://openalex.org/keywords/image-retrieval","display_name":"Image retrieval","score":0.536401629447937},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.42650145292282104},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.4196702539920807},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.33930790424346924},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3303728997707367},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1489100456237793}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7712219953536987},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7504345178604126},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7029165029525757},{"id":"https://openalex.org/C63435697","wikidata":"https://www.wikidata.org/wiki/Q864135","display_name":"Binary code","level":3,"score":0.6540495157241821},{"id":"https://openalex.org/C99138194","wikidata":"https://www.wikidata.org/wiki/Q183427","display_name":"Hash function","level":2,"score":0.6331263184547424},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5729171633720398},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.557922899723053},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5512340068817139},{"id":"https://openalex.org/C1667742","wikidata":"https://www.wikidata.org/wiki/Q10927554","display_name":"Image retrieval","level":3,"score":0.536401629447937},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.42650145292282104},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.4196702539920807},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.33930790424346924},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3303728997707367},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1489100456237793},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.0},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/ijcnn.2016.7727561","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2016.7727561","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1604.07060","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1604.07060","pdf_url":"https://arxiv.org/pdf/1604.07060","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":"mag:2344987838","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1604.07060.pdf","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.1604.07060","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1604.07060","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:1604.07060","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1604.07060","pdf_url":"https://arxiv.org/pdf/1604.07060","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":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2344987838.pdf"},"referenced_works_count":54,"referenced_works":["https://openalex.org/W746384004","https://openalex.org/W1468978781","https://openalex.org/W1502916507","https://openalex.org/W1522301498","https://openalex.org/W1606347560","https://openalex.org/W1756600408","https://openalex.org/W1913628733","https://openalex.org/W1951304353","https://openalex.org/W1956333070","https://openalex.org/W1983003547","https://openalex.org/W1985473611","https://openalex.org/W1992371516","https://openalex.org/W2005107501","https://openalex.org/W2036109700","https://openalex.org/W2036924016","https://openalex.org/W2049352011","https://openalex.org/W2074668987","https://openalex.org/W2083406700","https://openalex.org/W2089575713","https://openalex.org/W2095705004","https://openalex.org/W2096171208","https://openalex.org/W2100495367","https://openalex.org/W2131748184","https://openalex.org/W2145094598","https://openalex.org/W2156111011","https://openalex.org/W2171790913","https://openalex.org/W2172177969","https://openalex.org/W2202393049","https://openalex.org/W2207125444","https://openalex.org/W2221852422","https://openalex.org/W2259336176","https://openalex.org/W2268345094","https://openalex.org/W2293824885","https://openalex.org/W2913932916","https://openalex.org/W2951617802","https://openalex.org/W2964155457","https://openalex.org/W2971379523","https://openalex.org/W6601785968","https://openalex.org/W6629956336","https://openalex.org/W6631190155","https://openalex.org/W6631943919","https://openalex.org/W6636358008","https://openalex.org/W6637755507","https://openalex.org/W6671189819","https://openalex.org/W6674330103","https://openalex.org/W6681096077","https://openalex.org/W6682488304","https://openalex.org/W6684181414","https://openalex.org/W6688152296","https://openalex.org/W6688844344","https://openalex.org/W6697060382","https://openalex.org/W6697214482","https://openalex.org/W6712185225","https://openalex.org/W6767573829"],"related_works":["https://openalex.org/W2396976214","https://openalex.org/W2558208229","https://openalex.org/W2779880100","https://openalex.org/W2894089599","https://openalex.org/W2794807105","https://openalex.org/W3004566834","https://openalex.org/W2931053602","https://openalex.org/W2939001550","https://openalex.org/W2791929572","https://openalex.org/W2464915613","https://openalex.org/W3175708998","https://openalex.org/W1552157553","https://openalex.org/W2998777824","https://openalex.org/W1488497842","https://openalex.org/W3173326369","https://openalex.org/W2943956563","https://openalex.org/W2803163484","https://openalex.org/W3163572252","https://openalex.org/W2001286579","https://openalex.org/W3141336300"],"abstract_inverted_index":{"A":[0],"Content-Based":[1],"Image":[2],"Retrieval":[3],"(CBIR)":[4],"system":[5],"which":[6,67,78,232],"identifies":[7],"similar":[8],"medical":[9,74],"images":[10,129,240],"based":[11],"on":[12,136],"a":[13,85,96,111,117,154,158,199,242],"query":[14],"image":[15,47,223],"can":[16],"assist":[17],"clinicians":[18],"for":[19,73,88,147,180],"more":[20,137],"accurate":[21],"diagnosis.":[22],"The":[23,76],"recent":[24],"CBIR":[25],"research":[26],"trend":[27],"favors":[28],"the":[29,43,51,92,149,171,177,191,194,212,222,236],"construction":[30,94],"and":[31,100,125,157,185],"use":[32,192],"of":[33,54,62,193,239],"binary":[34,89,131,259],"codes":[35,179,196],"to":[36,70,127,163,169],"represent":[37],"images.":[38,75,141],"Deep":[39],"architectures":[40],"could":[41,210],"learn":[42],"non-linear":[44],"relationship":[45],"among":[46],"pixels":[48],"adaptively,":[49],"allowing":[50],"automatic":[52],"learning":[53],"high-level":[55],"features":[56],"from":[57,235],"raw":[58],"pixels.":[59],"However,":[60],"most":[61],"them":[63],"require":[64],"class":[65,82,144],"labels,":[66],"are":[68,233],"expensive":[69],"obtain,":[71],"particularly":[72],"methods":[77],"do":[79],"not":[80],"need":[81],"labels":[83,145],"utilize":[84],"deep":[86,112],"autoencoder":[87,114],"hashing,":[90],"but":[91],"code":[93],"involves":[95],"specific":[97],"training":[98,120,208],"algorithm":[99],"an":[101],"ad-hoc":[102],"regularization":[103],"technique.":[104],"In":[105],"this":[106],"study,":[107],"we":[108,152,167,226],"explored":[109],"using":[110,122,143,176,241],"de-noising":[113,243],"(DDA),":[115],"with":[116,190,257],"new":[118,207],"unsupervised":[119,165],"scheme":[121,209],"only":[123,146],"backpropagation":[124],"dropout,":[126],"hash":[128],"into":[130],"codes.":[132,260],"We":[133,203],"conducted":[134],"experiments":[135],"than":[138],"14,000":[139],"x-ray":[140],"By":[142],"evaluating":[148],"retrieval":[150,181,214,224,252],"results,":[151],"constructed":[153],"16-bit":[155,195],"DDA":[156,160,258],"512-bit":[159,178],"independently.":[161],"Comparing":[162],"other":[164],"methods,":[166],"succeeded":[168],"obtain":[170],"lowest":[172],"total":[173,201,213],"error":[174,215],"by":[175,217],"via":[182],"exhaustive":[183],"search,":[184],"speed":[186],"up":[187],"9.27":[188],"times":[189],"while":[197],"keeping":[198],"comparable":[200],"error.":[202],"found":[204],"that":[205],"our":[206],"reduce":[211],"significantly":[216],"21.9%.":[218],"To":[219],"further":[220],"boost":[221],"performance,":[225],"developed":[227],"Radon":[228,237],"Autoencoder":[229],"Barcode":[230],"(RABC)":[231],"learned":[234],"projections":[238],"autoencoder.":[244],"Experimental":[245],"results":[246],"demonstrated":[247],"its":[248],"superior":[249],"performance":[250],"in":[251],"when":[253],"it":[254],"was":[255],"combined":[256]},"counts_by_year":[{"year":2020,"cited_by_count":2},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":4},{"year":2016,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}