{"id":"https://openalex.org/W2210299817","doi":"https://doi.org/10.1007/978-3-319-24888-2_11","title":"HEp-2 Staining Pattern Recognition Using Stacked Fisher Network for Encoding Weber Local Descriptor","display_name":"HEp-2 Staining Pattern Recognition Using Stacked Fisher Network for Encoding Weber Local Descriptor","publication_year":2015,"publication_date":"2015-01-01","ids":{"openalex":"https://openalex.org/W2210299817","doi":"https://doi.org/10.1007/978-3-319-24888-2_11","mag":"2210299817"},"language":"en","primary_location":{"id":"doi:10.1007/978-3-319-24888-2_11","is_oa":true,"landing_page_url":"https://doi.org/10.1007/978-3-319-24888-2_11","pdf_url":null,"source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"book series"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lecture Notes in Computer Science","raw_type":"book-chapter"},"type":"book-chapter","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1007/978-3-319-24888-2_11","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5086851360","display_name":"Xian\u2010Hua Han","orcid":"https://orcid.org/0000-0002-5003-3180"},"institutions":[{"id":"https://openalex.org/I135768898","display_name":"Ritsumeikan University","ror":"https://ror.org/0197nmd03","country_code":"JP","type":"education","lineage":["https://openalex.org/I135768898","https://openalex.org/I4390039241"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Xian-Hua Han","raw_affiliation_strings":["Ritsumeikan University, 1-1-1, NojiHigashi, Kusatsu, Shiga, 525-8577, Japan"],"affiliations":[{"raw_affiliation_string":"Ritsumeikan University, 1-1-1, NojiHigashi, Kusatsu, Shiga, 525-8577, Japan","institution_ids":["https://openalex.org/I135768898"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044216245","display_name":"Yen\u2010Wei Chen","orcid":"https://orcid.org/0000-0002-5952-0188"},"institutions":[{"id":"https://openalex.org/I135768898","display_name":"Ritsumeikan University","ror":"https://ror.org/0197nmd03","country_code":"JP","type":"education","lineage":["https://openalex.org/I135768898","https://openalex.org/I4390039241"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yen-Wei Chen","raw_affiliation_strings":["Ritsumeikan University, 1-1-1, NojiHigashi, Kusatsu, Shiga, 525-8577, Japan"],"affiliations":[{"raw_affiliation_string":"Ritsumeikan University, 1-1-1, NojiHigashi, Kusatsu, Shiga, 525-8577, Japan","institution_ids":["https://openalex.org/I135768898"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087493208","display_name":"Gang Xu","orcid":"https://orcid.org/0000-0001-9875-051X"},"institutions":[{"id":"https://openalex.org/I135768898","display_name":"Ritsumeikan University","ror":"https://ror.org/0197nmd03","country_code":"JP","type":"education","lineage":["https://openalex.org/I135768898","https://openalex.org/I4390039241"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Gang Xu","raw_affiliation_strings":["Ritsumeikan University, 1-1-1, NojiHigashi, Kusatsu, Shiga, 525-8577, Japan"],"affiliations":[{"raw_affiliation_string":"Ritsumeikan University, 1-1-1, NojiHigashi, Kusatsu, Shiga, 525-8577, Japan","institution_ids":["https://openalex.org/I135768898"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5086851360"],"corresponding_institution_ids":["https://openalex.org/I135768898"],"apc_list":{"value":5000,"currency":"EUR","value_usd":5392},"apc_paid":{"value":5000,"currency":"EUR","value_usd":5392},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.07346327,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"85","last_page":"93"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13114","display_name":"Image Processing Techniques and Applications","score":0.9995999932289124,"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":0.9995999932289124,"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/T10580","display_name":"Immunotherapy and Immune Responses","score":0.9916999936103821,"subfield":{"id":"https://openalex.org/subfields/2403","display_name":"Immunology"},"field":{"id":"https://openalex.org/fields/24","display_name":"Immunology and Microbiology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9905999898910522,"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/computer-science","display_name":"Computer science","score":0.7845509052276611},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6978999376296997},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.658755898475647},{"id":"https://openalex.org/keywords/fisher-kernel","display_name":"Fisher kernel","score":0.5291944742202759},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4663834571838379},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.4480375647544861},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.34230417013168335},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1328078806400299},{"id":"https://openalex.org/keywords/kernel-fisher-discriminant-analysis","display_name":"Kernel Fisher discriminant analysis","score":0.09887483716011047},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09678405523300171}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7845509052276611},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6978999376296997},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.658755898475647},{"id":"https://openalex.org/C207798031","wikidata":"https://www.wikidata.org/wiki/Q8563425","display_name":"Fisher kernel","level":5,"score":0.5291944742202759},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4663834571838379},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.4480375647544861},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.34230417013168335},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1328078806400299},{"id":"https://openalex.org/C181367576","wikidata":"https://www.wikidata.org/wiki/Q6394184","display_name":"Kernel Fisher discriminant analysis","level":4,"score":0.09887483716011047},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09678405523300171}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/978-3-319-24888-2_11","is_oa":true,"landing_page_url":"https://doi.org/10.1007/978-3-319-24888-2_11","pdf_url":null,"source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"book series"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lecture Notes in Computer Science","raw_type":"book-chapter"}],"best_oa_location":{"id":"doi:10.1007/978-3-319-24888-2_11","is_oa":true,"landing_page_url":"https://doi.org/10.1007/978-3-319-24888-2_11","pdf_url":null,"source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"book series"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lecture Notes in Computer Science","raw_type":"book-chapter"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.7400000095367432,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W97368233","https://openalex.org/W1619841130","https://openalex.org/W2030105367","https://openalex.org/W2053544201","https://openalex.org/W2086012758","https://openalex.org/W2122092925","https://openalex.org/W2130258210","https://openalex.org/W2134199473","https://openalex.org/W2138798535","https://openalex.org/W2151103935","https://openalex.org/W2162915993"],"related_works":["https://openalex.org/W2347824352","https://openalex.org/W2112875849","https://openalex.org/W4252601687","https://openalex.org/W2352052469","https://openalex.org/W2137772040","https://openalex.org/W2347560116","https://openalex.org/W2163952607","https://openalex.org/W28436895","https://openalex.org/W2088386272","https://openalex.org/W2022911502"],"abstract_inverted_index":{"This":[0,52],"study":[1,53],"addresses":[2],"the":[3,7,19,28,32,45,50,97,105,118,122,127,131,143,163,181,184,197,204,209,219,224,234],"recognition":[4,61,78,226],"problem":[5],"of":[6,21,49,96,121,130,165,183],"HEp-2":[8,59,199],"cell":[9,60,200],"using":[10,196],"indirect":[11],"immunofluorescent":[12],"(IIF)":[13],"image":[14,99,174],"analysis,":[15],"which":[16,64,101],"can":[17,212],"indicate":[18],"presence":[20],"autoimmune":[22],"diseases":[23],"by":[24],"finding":[25],"antibodies":[26],"in":[27,63,76,142,203],"patient":[29],"serum.":[30],"Generally,":[31],"method":[33],"used":[34],"for":[35,110,173,191],"IIF":[36],"analysis":[37],"remains":[38],"subjective,":[39],"and":[40,47,162,222],"depends":[41,114],"too":[42],"heavily":[43],"on":[44,104,117,126],"experience":[46],"expertise":[48],"physician.":[51],"aims":[54],"to":[55,66,82],"explore":[56],"an":[57],"automatic":[58],"system,":[62],"how":[65],"extract":[67],"highly":[68],"discriminate":[69,193],"visual":[70],"features":[71],"plays":[72],"a":[73,91,112,134,153,158,214],"key":[74],"role":[75],"this":[77,84],"application.":[79],"In":[80],"order":[81],"realize":[83],"purpose,":[85],"our":[86],"main":[87],"efforts":[88],"include:":[89],"(1)":[90],"transformed":[92],"excitation":[93,144],"domain":[94],"instead":[95],"raw":[98],"domain,":[100,145],"is":[102,229],"based":[103],"fact":[106],"that":[107,208,223],"human":[108],"perception":[109],"disguising":[111],"pattern":[113],"not":[115,166],"only":[116,167],"absolute":[119],"intensity":[120],"stimulus":[123],"but":[124,136,169],"also":[125,170],"relative":[128],"variance":[129],"stimulus;":[132],"(2)":[133],"simple":[135],"robust":[137],"micro-texton":[138],"without":[139],"any":[140],"quantization":[141],"called":[146,176],"as":[147,177],"Weber":[148],"local":[149],"descriptor":[150],"(WLD);":[151],"(3)":[152],"data-driven":[154],"coding":[155],"strategy":[156,211],"with":[157],"parametric":[159],"probability":[160],"process,":[161],"extraction":[164],"low-":[168],"high-order":[171],"statistics":[172],"representation":[175],"Fisher":[178,185],"vector;":[179],"(4)":[180],"stacking":[182],"network":[186],"into":[187],"deep":[188],"learning":[189],"framework":[190],"more":[192],"feature.":[194],"Experiments":[195],"open":[198],"dataset":[201],"released":[202],"ICIP2013":[205],"contest":[206],"validate":[207],"proposed":[210],"achieve":[213],"much":[215],"better":[216],"performance":[217],"than":[218],"state-of-the-art":[220],"approaches,":[221],"achieved":[225],"error":[227],"rate":[228],"even":[230],"very":[231],"significantly":[232],"below":[233],"observed":[235],"intra-laboratory":[236],"variability":[237]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
