{"id":"https://openalex.org/W2793785117","doi":"https://doi.org/10.1117/12.2293887","title":"Training classifiers with limited data using the Radon cumulative distribution transform","display_name":"Training classifiers with limited data using the Radon cumulative distribution transform","publication_year":2018,"publication_date":"2018-03-02","ids":{"openalex":"https://openalex.org/W2793785117","doi":"https://doi.org/10.1117/12.2293887","mag":"2793785117"},"language":"en","primary_location":{"id":"doi:10.1117/12.2293887","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2293887","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2018: Image Processing","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/A5024240868","display_name":"Cailey E. Fitzgerald","orcid":null},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Cailey E. Fitzgerald","raw_affiliation_strings":["Univ. of Virginia (United States)"],"affiliations":[{"raw_affiliation_string":"Univ. of Virginia (United States)","institution_ids":["https://openalex.org/I51556381"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041274548","display_name":"Gustavo K. Rohde","orcid":"https://orcid.org/0000-0003-1703-9035"},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gustavo K. Rohde","raw_affiliation_strings":["Univ. of Virginia (United States)"],"affiliations":[{"raw_affiliation_string":"Univ. of Virginia (United States)","institution_ids":["https://openalex.org/I51556381"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020295713","display_name":"Liam Cattell","orcid":"https://orcid.org/0000-0001-7772-2851"},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Liam C. Cattell","raw_affiliation_strings":["Univ. of Virginia (United States)"],"affiliations":[{"raw_affiliation_string":"Univ. of Virginia (United States)","institution_ids":["https://openalex.org/I51556381"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5024240868"],"corresponding_institution_ids":["https://openalex.org/I51556381"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.02785762,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"12","issue":null,"first_page":"118","last_page":"118"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9983999729156494,"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":0.9983999729156494,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.991599977016449,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9890999794006348,"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/radon-transform","display_name":"Radon transform","score":0.6905434131622314},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6880188584327698},{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.6782354116439819},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6368236541748047},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6292172074317932},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.601695716381073},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5892552733421326},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5836525559425354},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.5527698397636414},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5275879502296448},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.47175389528274536},{"id":"https://openalex.org/keywords/radon","display_name":"Radon","score":0.4213009178638458},{"id":"https://openalex.org/keywords/cumulative-distribution-function","display_name":"Cumulative distribution function","score":0.4186418950557709},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3294829726219177},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.299441397190094},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.22780853509902954},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.20829716324806213},{"id":"https://openalex.org/keywords/probability-density-function","display_name":"Probability density function","score":0.11916723847389221}],"concepts":[{"id":"https://openalex.org/C197231052","wikidata":"https://www.wikidata.org/wiki/Q979829","display_name":"Radon transform","level":2,"score":0.6905434131622314},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6880188584327698},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.6782354116439819},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6368236541748047},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6292172074317932},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.601695716381073},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5892552733421326},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5836525559425354},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.5527698397636414},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5275879502296448},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.47175389528274536},{"id":"https://openalex.org/C545943180","wikidata":"https://www.wikidata.org/wiki/Q1133","display_name":"Radon","level":2,"score":0.4213009178638458},{"id":"https://openalex.org/C103784038","wikidata":"https://www.wikidata.org/wiki/Q386228","display_name":"Cumulative distribution function","level":3,"score":0.4186418950557709},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3294829726219177},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.299441397190094},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.22780853509902954},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.20829716324806213},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.11916723847389221},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2293887","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2293887","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2018: Image Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W2101234009","https://openalex.org/W2112796928","https://openalex.org/W2141855440","https://openalex.org/W2240450848","https://openalex.org/W2267271843","https://openalex.org/W2353048332","https://openalex.org/W2792802628","https://openalex.org/W6637373629","https://openalex.org/W6675354045","https://openalex.org/W6680925131"],"related_works":["https://openalex.org/W4386603768","https://openalex.org/W2950475743","https://openalex.org/W2886711096","https://openalex.org/W2750384547","https://openalex.org/W4380078352","https://openalex.org/W3046591097","https://openalex.org/W4389249638","https://openalex.org/W2733410219","https://openalex.org/W2734358244","https://openalex.org/W3194633786"],"abstract_inverted_index":{"Our":[0],"purpose":[1],"in":[2,125,197,205,208,219],"this":[3,53],"study":[4],"is":[5,215],"to":[6,44,51,129,136,190,217],"investigate":[7],"whether":[8],"a":[9,26,57,64,111,150,156,182,187],"recently":[10],"introduced":[11],"image":[12,206],"transform,":[13],"denoted":[14],"as":[15,25,213],"the":[16,32,46,49,87,97,132,161,168,178],"Radon":[17,198],"cumulative":[18,199],"distribution":[19,200],"transform":[20,201],"(R-CDT),":[21],"can":[22],"be":[23,137],"used":[24,100,138],"viable":[27],"preprocessing":[28],"step":[29],"for":[30,101,139],"augmenting":[31],"robustness":[33],"of":[34,48,68,76,96,113,116,131,210],"training":[35,40,90,105,117],"end-to-end":[36],"systems":[37],"with":[38,94,155,181],"fewer":[39],"samples.":[41],"In":[42],"order":[43],"assess":[45],"ability":[47],"R-CDT":[50,179],"perform":[52],"aim,":[54],"we":[55,119],"identified":[56],"standard":[58],"machine":[59,153],"learning":[60],"dataset,":[61],"MNIST,":[62],"and":[63,91,149,164,185],"preliminary":[65],"dataset":[66,134],"comprised":[67],"liver":[69],"cell":[70],"nuclei":[71],"images":[72,163,165],"derived":[73],"from":[74,127],"one":[75,214],"two":[77],"tissue":[78],"types:":[79],"benign":[80],"or":[81],"malignant":[82],"tumor":[83],"lesions.":[84],"We":[85],"separated":[86],"data":[88,99,122],"into":[89],"testing":[92,102],"sets":[93],"20%":[95],"total":[98,133],"across":[103],"all":[104],"set":[106],"size":[107,115,126,135],"conditions.":[108],"To":[109],"simulate":[110],"range":[112],"limited":[114,211],"examples,":[118],"randomly":[120],"generated":[121],"subsets":[123],"ranging":[124],"80%":[128],"0.8%":[130],"training.":[140],"Linear":[141],"classification":[142,172,196,204],"algorithms":[143],"were":[144,174],"implemented":[145],"via":[146,167,176],"logistic":[147],"regression":[148],"support":[151],"vector":[152],"model":[154],"linear":[157],"kernel":[158],"on":[159],"both":[160],"raw":[162],"transformed":[166],"R-CDT.":[169],"Additionaly,":[170],"non-linear":[171],"accuracies":[173],"assessed":[175],"comparing":[177],"paired":[180],"shallow":[183],"CNN":[184,189],"using":[186],"deep":[188],"classify":[191],"images.":[192],"Results":[193],"indicate":[194],"that":[195],"space":[202,207],"outperforms":[203],"conditions":[209],"data,":[212],"likely":[216],"see":[218],"medical":[220],"imaging.":[221]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}