{"id":"https://openalex.org/W2790277825","doi":"https://doi.org/10.1109/icip.2017.8296505","title":"Active convolutional neural networks for cancerous tissue recognition","display_name":"Active convolutional neural networks for cancerous tissue recognition","publication_year":2017,"publication_date":"2017-09-01","ids":{"openalex":"https://openalex.org/W2790277825","doi":"https://doi.org/10.1109/icip.2017.8296505","mag":"2790277825"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2017.8296505","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2017.8296505","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5004868519","display_name":"Panagiotis Stanitsas","orcid":null},"institutions":[{"id":"https://openalex.org/I2800403580","display_name":"University of Minnesota System","ror":"https://ror.org/03grvy078","country_code":"US","type":"education","lineage":["https://openalex.org/I2800403580"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Panagiotis Stanitsas","raw_affiliation_strings":["University of Minnesota"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Minnesota","institution_ids":["https://openalex.org/I2800403580"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024613828","display_name":"Anoop Cherian","orcid":"https://orcid.org/0000-0002-5566-0351"},"institutions":[{"id":"https://openalex.org/I118347636","display_name":"Australian National University","ror":"https://ror.org/019wvm592","country_code":"AU","type":"education","lineage":["https://openalex.org/I118347636"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Anoop Cherian","raw_affiliation_strings":["Australian National University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Australian National University","institution_ids":["https://openalex.org/I118347636"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109224669","display_name":"Alexander M. Truskinovsky","orcid":null},"institutions":[{"id":"https://openalex.org/I1292894508","display_name":"Roswell Park Comprehensive Cancer Center","ror":"https://ror.org/0499dwk57","country_code":"US","type":"facility","lineage":["https://openalex.org/I1292894508"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alexander Truskinovsky","raw_affiliation_strings":["Roswell Park Cancer Institute"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Roswell Park Cancer Institute","institution_ids":["https://openalex.org/I1292894508"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081890915","display_name":"Vassilios Morellas","orcid":"https://orcid.org/0000-0002-9198-8442"},"institutions":[{"id":"https://openalex.org/I2800403580","display_name":"University of Minnesota System","ror":"https://ror.org/03grvy078","country_code":"US","type":"education","lineage":["https://openalex.org/I2800403580"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vassilios Morellas","raw_affiliation_strings":["University of Minnesota"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Minnesota","institution_ids":["https://openalex.org/I2800403580"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109170958","display_name":"Nikolaos Papanikolopoulos","orcid":null},"institutions":[{"id":"https://openalex.org/I2800403580","display_name":"University of Minnesota System","ror":"https://ror.org/03grvy078","country_code":"US","type":"education","lineage":["https://openalex.org/I2800403580"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nikolaos Papanikolopoulos","raw_affiliation_strings":["University of Minnesota"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Minnesota","institution_ids":["https://openalex.org/I2800403580"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"2","issue":null,"first_page":"1367","last_page":"1371"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9975000023841858,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9975000023841858,"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/T10862","display_name":"AI in cancer detection","score":0.9968000054359436,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9965999722480774,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8254883289337158},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7908796072006226},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6915799379348755},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.6068555116653442},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5429761409759521},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5283889174461365},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.48536789417266846},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4574347138404846},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4365697503089905},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.43104618787765503},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.42917072772979736},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.42179927229881287},{"id":"https://openalex.org/keywords/stochastic-gradient-descent","display_name":"Stochastic gradient descent","score":0.42140302062034607},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3007301390171051},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07951122522354126}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8254883289337158},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7908796072006226},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6915799379348755},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.6068555116653442},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5429761409759521},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5283889174461365},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48536789417266846},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4574347138404846},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4365697503089905},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.43104618787765503},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.42917072772979736},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.42179927229881287},{"id":"https://openalex.org/C206688291","wikidata":"https://www.wikidata.org/wiki/Q7617819","display_name":"Stochastic gradient descent","level":3,"score":0.42140302062034607},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3007301390171051},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07951122522354126},{"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2017.8296505","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2017.8296505","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320331724","display_name":"Australian Centre for Robotic Vision","ror":"https://ror.org/02zv9xv82"},{"id":"https://openalex.org/F4320334704","display_name":"Australian Research Council","ror":"https://ror.org/05mmh0f86"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W146364421","https://openalex.org/W1686810756","https://openalex.org/W2011674654","https://openalex.org/W2016053056","https://openalex.org/W2019363670","https://openalex.org/W2025984714","https://openalex.org/W2026566343","https://openalex.org/W2060099436","https://openalex.org/W2113741880","https://openalex.org/W2114232233","https://openalex.org/W2124244761","https://openalex.org/W2129014109","https://openalex.org/W2138079527","https://openalex.org/W2140539195","https://openalex.org/W2155893237","https://openalex.org/W2216045586","https://openalex.org/W2471138382","https://openalex.org/W2565587197","https://openalex.org/W2592697089","https://openalex.org/W2607948581","https://openalex.org/W2919115771","https://openalex.org/W4237065586","https://openalex.org/W4238893454","https://openalex.org/W4285719527","https://openalex.org/W6605869242","https://openalex.org/W6676780844","https://openalex.org/W6720435453","https://openalex.org/W6731292462","https://openalex.org/W6734912205"],"related_works":["https://openalex.org/W2361861616","https://openalex.org/W2263699433","https://openalex.org/W2377979023","https://openalex.org/W2218034408","https://openalex.org/W2392921965","https://openalex.org/W2358755282","https://openalex.org/W2625833328","https://openalex.org/W1533177136","https://openalex.org/W4287755480","https://openalex.org/W2785875001"],"abstract_inverted_index":{"Deep":[0],"neural":[1],"networks":[2],"typically":[3],"require":[4],"large":[5,25],"amounts":[6],"of":[7,46,104],"annotated":[8,26,112],"data":[9,50],"to":[10,49,68],"be":[11],"trained":[12],"effectively.":[13],"However,":[14],"in":[15],"several":[16],"scientific":[17],"disciplines,":[18],"including":[19],"medical":[20],"image":[21],"analysis,":[22],"generating":[23],"such":[24],"datasets":[27,106],"requires":[28],"specialized":[29],"domain":[30],"knowledge,":[31],"and":[32,94],"hence":[33],"is":[34,67],"usually":[35],"very":[36],"expensive.":[37],"In":[38],"this":[39],"work,":[40],"we":[41,85],"present":[42],"a":[43],"novel":[44],"application":[45],"active":[47],"learning":[48],"sample":[51],"selection":[52],"for":[53,59,78],"training":[54,79,116],"Convolutional":[55],"Neural":[56],"Networks":[57],"(CNN)":[58],"Cancerous":[60],"Tissue":[61],"Recognition":[62],"(CTR).":[63],"Our":[64,98],"main":[65],"idea":[66],"steer":[69],"annotation":[70],"efforts":[71],"towards":[72],"selecting":[73],"the":[74,80,128],"most":[75],"informative":[76],"samples":[77],"CNN.":[81],"To":[82],"quantify":[83],"informativeness,":[84],"explore":[86],"three":[87,101],"choices":[88],"based":[89],"on":[90,100],"discrete":[91],"entropy,":[92],"best-vs-second-best,":[93],"k-nearest":[95],"neighbor":[96],"agreement.":[97],"results":[99],"different":[102],"types":[103],"cancer":[105],"consistently":[107],"demonstrate":[108],"that":[109],"under":[110],"limited":[111],"samples,":[113],"our":[114],"proposed":[115],"scheme":[117],"converges":[118],"faster":[119],"than":[120],"classical":[121],"randomized":[122],"stochastic":[123],"gradient":[124],"descent,":[125],"while":[126],"achieving":[127],"same":[129],"(or":[130],"sometimes":[131],"superior)":[132],"classification":[133],"accuracy.":[134]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}