{"id":"https://openalex.org/W2511151031","doi":"https://doi.org/10.1109/icip.2016.7532557","title":"Enhancement of low-resolution HEp-2 cell image classification using partial least-square regression","display_name":"Enhancement of low-resolution HEp-2 cell image classification using partial least-square regression","publication_year":2016,"publication_date":"2016-08-17","ids":{"openalex":"https://openalex.org/W2511151031","doi":"https://doi.org/10.1109/icip.2016.7532557","mag":"2511151031"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2016.7532557","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2016.7532557","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Image Processing (ICIP)","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/A5110586170","display_name":"Asaad M. Anam","orcid":null},"institutions":[{"id":"https://openalex.org/I145487455","display_name":"Cairo University","ror":"https://ror.org/03q21mh05","country_code":"EG","type":"education","lineage":["https://openalex.org/I145487455"]}],"countries":["EG"],"is_corresponding":true,"raw_author_name":"Asaad M. Anam","raw_affiliation_strings":["Department of Biomedical Engineering and Systems, Cairo University, Giza, Egypt"],"affiliations":[{"raw_affiliation_string":"Department of Biomedical Engineering and Systems, Cairo University, Giza, Egypt","institution_ids":["https://openalex.org/I145487455"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090009857","display_name":"Muhammad A. Rushdi","orcid":"https://orcid.org/0000-0001-9869-0270"},"institutions":[{"id":"https://openalex.org/I145487455","display_name":"Cairo University","ror":"https://ror.org/03q21mh05","country_code":"EG","type":"education","lineage":["https://openalex.org/I145487455"]}],"countries":["EG"],"is_corresponding":false,"raw_author_name":"Muhammad A. Rushdi","raw_affiliation_strings":["Department of Biomedical Engineering and Systems, Cairo University, Giza, Egypt"],"affiliations":[{"raw_affiliation_string":"Department of Biomedical Engineering and Systems, Cairo University, Giza, Egypt","institution_ids":["https://openalex.org/I145487455"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112368923","display_name":"Ahmed S. Fahmy","orcid":null},"institutions":[{"id":"https://openalex.org/I145487455","display_name":"Cairo University","ror":"https://ror.org/03q21mh05","country_code":"EG","type":"education","lineage":["https://openalex.org/I145487455"]}],"countries":["EG"],"is_corresponding":false,"raw_author_name":"Ahmed S. Fahmy","raw_affiliation_strings":["Department of Biomedical Engineering and Systems, Cairo University, Giza, Egypt"],"affiliations":[{"raw_affiliation_string":"Department of Biomedical Engineering and Systems, Cairo University, Giza, Egypt","institution_ids":["https://openalex.org/I145487455"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5110586170"],"corresponding_institution_ids":["https://openalex.org/I145487455"],"apc_list":null,"apc_paid":null,"fwci":0.6459,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.75070334,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1245","last_page":"1249"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13114","display_name":"Image Processing Techniques and Applications","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13114","display_name":"Image Processing Techniques and Applications","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10308","display_name":"Systemic Lupus Erythematosus Research","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/2745","display_name":"Rheumatology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","score":0.9902999997138977,"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/subspace-topology","display_name":"Subspace topology","score":0.6900320053100586},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6561428904533386},{"id":"https://openalex.org/keywords/canonical-correlation","display_name":"Canonical correlation","score":0.6480076909065247},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6479113101959229},{"id":"https://openalex.org/keywords/interpolation","display_name":"Interpolation (computer graphics)","score":0.6258756518363953},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6257418990135193},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.5768008828163147},{"id":"https://openalex.org/keywords/resolution","display_name":"Resolution (logic)","score":0.5713719129562378},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5147888660430908},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.45120275020599365},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.42934268712997437},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.42583584785461426},{"id":"https://openalex.org/keywords/high-resolution","display_name":"High resolution","score":0.4215271770954132},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.24263912439346313},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.08423963189125061}],"concepts":[{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.6900320053100586},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6561428904533386},{"id":"https://openalex.org/C153874254","wikidata":"https://www.wikidata.org/wiki/Q115542","display_name":"Canonical correlation","level":2,"score":0.6480076909065247},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6479113101959229},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.6258756518363953},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6257418990135193},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.5768008828163147},{"id":"https://openalex.org/C138268822","wikidata":"https://www.wikidata.org/wiki/Q1051925","display_name":"Resolution (logic)","level":2,"score":0.5713719129562378},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5147888660430908},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.45120275020599365},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.42934268712997437},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.42583584785461426},{"id":"https://openalex.org/C3020199158","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"High resolution","level":2,"score":0.4215271770954132},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24263912439346313},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.08423963189125061},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2016.7532557","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2016.7532557","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4099999964237213,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1999129663","https://openalex.org/W2007442867","https://openalex.org/W2030105367","https://openalex.org/W2030899956","https://openalex.org/W2049380881","https://openalex.org/W2056052642","https://openalex.org/W2073136475","https://openalex.org/W2083657345","https://openalex.org/W2123904821","https://openalex.org/W2129680231","https://openalex.org/W2136995369","https://openalex.org/W2150706391","https://openalex.org/W2167277498","https://openalex.org/W2167803572","https://openalex.org/W2243324510","https://openalex.org/W2294556704","https://openalex.org/W3102042549","https://openalex.org/W4229500742"],"related_works":["https://openalex.org/W1565185441","https://openalex.org/W1980381208","https://openalex.org/W1968846550","https://openalex.org/W302711736","https://openalex.org/W2313359725","https://openalex.org/W2375550484","https://openalex.org/W2364594919","https://openalex.org/W2123957576","https://openalex.org/W2092806427","https://openalex.org/W2046697080"],"abstract_inverted_index":{"Automated":[0],"classification":[1,21,108],"of":[2,13,37,46,52],"HEp-2":[3,31,72,86],"cell":[4],"images":[5,32,39,54],"is":[6],"crucial":[7],"for":[8],"fast":[9],"and":[10,44,84,101,119],"accurate":[11],"detection":[12],"autoimmune":[14],"diseases.":[15],"Recent":[16],"competitions":[17],"resulted":[18],"in":[19,57,107,114],"high":[20],"rates":[22],"on":[23,29],"publicly":[24],"available":[25],"datasets.":[26],"However,":[27],"performance":[28,59,109],"low-resolution":[30,53,71],"typically":[33],"lagged":[34],"behind":[35],"that":[36],"high-resolution":[38,68,85],"due":[40,110],"to":[41,66,80,111],"the":[42,58,95],"blurring":[43],"sub-sampling":[45],"fine":[47],"cellular":[48],"details.":[49],"Direct":[50],"interpolation":[51,118],"couldn't":[55],"fill":[56],"gap.":[60],"We":[61],"propose":[62],"a":[63,90],"learning-based":[64],"approach":[65,75],"infer":[67],"features":[69,88,96],"from":[70],"images.":[73],"Our":[74],"exploits":[76],"partial":[77],"least-square":[78],"(PLS)":[79],"linearly":[81],"project":[82],"low-":[83],"image":[87],"into":[89],"common":[91],"linear":[92],"subspace":[93],"where":[94],"are":[97],"highly":[98],"correlated.":[99],"Experimental":[100],"statistical":[102],"evaluations":[103],"show":[104],"significant":[105],"improvement":[106],"our":[112],"technique":[113],"comparison":[115],"with":[116],"direct":[117],"canonical":[120],"correlation":[121],"analysis":[122],"techniques.":[123]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}