{"id":"https://openalex.org/W3120688585","doi":"https://doi.org/10.1109/icarcv50220.2020.9305306","title":"An Improved Micro-Calcification Detection Algorithm Using a Novel Multifractal Texture Descriptor and CNN","display_name":"An Improved Micro-Calcification Detection Algorithm Using a Novel Multifractal Texture Descriptor and CNN","publication_year":2020,"publication_date":"2020-12-13","ids":{"openalex":"https://openalex.org/W3120688585","doi":"https://doi.org/10.1109/icarcv50220.2020.9305306","mag":"3120688585"},"language":"en","primary_location":{"id":"doi:10.1109/icarcv50220.2020.9305306","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icarcv50220.2020.9305306","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV)","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/A5100363264","display_name":"Haipeng Li","orcid":"https://orcid.org/0000-0001-6443-6098"},"institutions":[{"id":"https://openalex.org/I185492890","display_name":"University of Canterbury","ror":"https://ror.org/03y7q9t39","country_code":"NZ","type":"education","lineage":["https://openalex.org/I185492890"]}],"countries":["NZ"],"is_corresponding":true,"raw_author_name":"Haipeng Li","raw_affiliation_strings":["Computer Science and Software Engineering Department, University of Canterbury, Christchurch, New Zealand"],"affiliations":[{"raw_affiliation_string":"Computer Science and Software Engineering Department, University of Canterbury, Christchurch, New Zealand","institution_ids":["https://openalex.org/I185492890"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010870517","display_name":"Ramakrishnan Mukundan","orcid":"https://orcid.org/0000-0003-4578-1931"},"institutions":[{"id":"https://openalex.org/I185492890","display_name":"University of Canterbury","ror":"https://ror.org/03y7q9t39","country_code":"NZ","type":"education","lineage":["https://openalex.org/I185492890"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"Ramakrishnan Mukundan","raw_affiliation_strings":["Computer Science and Software Engineering Department, University of Canterbury, Christchurch, New Zealand"],"affiliations":[{"raw_affiliation_string":"Computer Science and Software Engineering Department, University of Canterbury, Christchurch, New Zealand","institution_ids":["https://openalex.org/I185492890"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054753555","display_name":"Shelley Boyd","orcid":"https://orcid.org/0000-0001-6476-3355"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shelley Boyd","raw_affiliation_strings":["Canterbury Breastcare, St. George's Medical Center, Christchurch, New Zealand"],"affiliations":[{"raw_affiliation_string":"Canterbury Breastcare, St. George's Medical Center, Christchurch, New Zealand","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100363264"],"corresponding_institution_ids":["https://openalex.org/I185492890"],"apc_list":null,"apc_paid":null,"fwci":0.2651,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.65296081,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"4","issue":null,"first_page":"654","last_page":"659"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9998999834060669,"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/T10862","display_name":"AI in cancer detection","score":0.9998999834060669,"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/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.9947999715805054,"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":0.9901999831199646,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7406023740768433},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7202105522155762},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7158001065254211},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.678626537322998},{"id":"https://openalex.org/keywords/false-positive-paradox","display_name":"False positive paradox","score":0.6739620566368103},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5987290143966675},{"id":"https://openalex.org/keywords/multifractal-system","display_name":"Multifractal system","score":0.5158895254135132},{"id":"https://openalex.org/keywords/receiver-operating-characteristic","display_name":"Receiver operating characteristic","score":0.49618062376976013},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.4921550154685974},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.48199573159217834},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4150463342666626},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.24237948656082153},{"id":"https://openalex.org/keywords/fractal","display_name":"Fractal","score":0.20663705468177795},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.16622591018676758},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.14261439442634583}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7406023740768433},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7202105522155762},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7158001065254211},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.678626537322998},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.6739620566368103},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5987290143966675},{"id":"https://openalex.org/C133905733","wikidata":"https://www.wikidata.org/wiki/Q2629238","display_name":"Multifractal system","level":3,"score":0.5158895254135132},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.49618062376976013},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.4921550154685974},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.48199573159217834},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4150463342666626},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24237948656082153},{"id":"https://openalex.org/C40636538","wikidata":"https://www.wikidata.org/wiki/Q81392","display_name":"Fractal","level":2,"score":0.20663705468177795},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.16622591018676758},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.14261439442634583},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icarcv50220.2020.9305306","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icarcv50220.2020.9305306","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.6800000071525574,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W304373761","https://openalex.org/W1826701780","https://openalex.org/W1959490658","https://openalex.org/W2027902001","https://openalex.org/W2077770030","https://openalex.org/W2171522051","https://openalex.org/W2266695008","https://openalex.org/W2271473780","https://openalex.org/W2288550246","https://openalex.org/W2297940673","https://openalex.org/W2470704843","https://openalex.org/W2550143760","https://openalex.org/W2566219983","https://openalex.org/W2575693630","https://openalex.org/W2605466006","https://openalex.org/W2740492228","https://openalex.org/W2754066927","https://openalex.org/W2964189045","https://openalex.org/W3000840541"],"related_works":["https://openalex.org/W1639315488","https://openalex.org/W2056486084","https://openalex.org/W4382488079","https://openalex.org/W2042397562","https://openalex.org/W2356714366","https://openalex.org/W1977404842","https://openalex.org/W2370749423","https://openalex.org/W2168415552","https://openalex.org/W2078152912","https://openalex.org/W3145891640"],"abstract_inverted_index":{"Detecting":[0],"individual":[1,238],"micro-calcifications":[2],"(MCs)":[3],"in":[4,28,48,83,155,172,225,240],"mammograms":[5,239],"is":[6,98,127,185,216],"a":[7,25,54,57,62,93,102,121,212,241],"challenging":[8],"problem":[9],"due":[10],"to":[11,76,100,113,129,144,187],"heterogeneous":[12],"properties":[13],"and":[14,40,69,106,134,175,202,230,252],"diverse":[15],"composition":[16],"of":[17,31,42,56,80],"breast":[18],"tissues.":[19],"False":[20],"positives":[21],"(FPs)":[22],"are":[23,158,194],"therefore":[24],"common":[26],"occurrence":[27],"the":[29,43,90,107,118,161,189,205,220,226,231,246,254],"outputs":[30,61],"different":[32],"detectors.":[33],"This":[34],"paper":[35],"focuses":[36],"on":[37,237],"FP":[38,250],"reduction":[39],"improvement":[41],"final":[44],"MCs":[45,171],"detection":[46,234,256],"accuracy":[47,257],"mammograms.":[49],"The":[50],"proposed":[51,162,190,247],"method":[52,248],"uses":[53],"combination":[55],"MC":[58,67,81,115,142,233,255],"detector":[59],"which":[60,165],"patch":[63,223],"set":[64,224,243],"containing":[65],"candidate":[66,119],"spots,":[68],"alpha":[70,109,136,156,222],"images":[71,157],"derived":[72],"from":[73,117],"multifractal":[74,104],"analysis":[75],"enhance":[77],"texture":[78,91],"features":[79,153],"spots":[82,116,143],"each":[84],"target":[85],"patch.":[86],"For":[87],"further":[88],"highlighting":[89],"features,":[92],"Weber's":[94],"law":[95],"based":[96,236],"approach":[97],"used":[99,186],"construct":[101],"new":[103,221],"measure":[105],"corresponding":[108,135],"patches.":[110],"In":[111,209],"order":[112],"distinguish":[114],"set,":[120],"convolutional":[122],"neural":[123],"network":[124],"(CNN)":[125],"classifier":[126],"designed":[128,227],"process":[130],"original":[131],"mammogram":[132,182],"patches":[133,137],"together":[138],"for":[139,170],"classifying":[140],"suspicious":[141],"true":[145],"positive":[146,150],"group":[147],"or":[148],"false":[149],"group.":[151],"Multifractal":[152],"contained":[154],"fed":[159],"into":[160],"CNN":[163,228],"model,":[164],"facilitate":[166],"learning":[167],"richer":[168],"representations":[169],"local":[173],"regions":[174],"presenting":[176],"better":[177],"classification":[178,214],"performance.":[179],"A":[180],"digital":[181],"dataset,":[183],"INbreast,":[184],"test":[188,242],"method.":[191],"Experimental":[192],"results":[193,235],"evaluated":[195],"using":[196,219],"free-response":[197],"receiver":[198],"operating":[199],"characteristic":[200],"(FROC)":[201],"area":[203],"under":[204],"FROC":[206],"curve":[207],"(AUC).":[208],"our":[210],"experiments,":[211],"desirable":[213],"performance":[215],"observed":[217],"after":[218],"classifier,":[229],"general":[232],"demonstrate":[244],"that":[245],"reduces":[249],"numbers":[251],"improves":[253],"effectively.":[258]},"counts_by_year":[{"year":2023,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}