{"id":"https://openalex.org/W2963008443","doi":"https://doi.org/10.1109/ipta.2017.8310149","title":"Convolutional neural networks for histopathology image classification: Training vs. Using pre-trained networks","display_name":"Convolutional neural networks for histopathology image classification: Training vs. Using pre-trained networks","publication_year":2017,"publication_date":"2017-11-01","ids":{"openalex":"https://openalex.org/W2963008443","doi":"https://doi.org/10.1109/ipta.2017.8310149","mag":"2963008443"},"language":"en","primary_location":{"id":"doi:10.1109/ipta.2017.8310149","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ipta.2017.8310149","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA)","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/A5077658487","display_name":"Brady Kieffer","orcid":null},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Brady Kieffer","raw_affiliation_strings":["KIMIA Lab, University of Waterloo, ON, Canada"],"affiliations":[{"raw_affiliation_string":"KIMIA Lab, University of Waterloo, ON, Canada","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028824188","display_name":"Morteza Babaie","orcid":"https://orcid.org/0000-0002-6916-5941"},"institutions":[{"id":"https://openalex.org/I158248296","display_name":"Amirkabir University of Technology","ror":"https://ror.org/04gzbav43","country_code":"IR","type":"education","lineage":["https://openalex.org/I158248296"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Morteza Babaie","raw_affiliation_strings":["Mathematics and Computer Science Department, Amirkabir University of Technology, Tehran, Iran"],"affiliations":[{"raw_affiliation_string":"Mathematics and Computer Science Department, Amirkabir University of Technology, Tehran, Iran","institution_ids":["https://openalex.org/I158248296"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115592170","display_name":"Shivam Kalra","orcid":null},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Shivam Kalra","raw_affiliation_strings":["KIMIA Lab, University of Waterloo, ON, Canada"],"affiliations":[{"raw_affiliation_string":"KIMIA Lab, University of Waterloo, ON, Canada","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038566332","display_name":"Hamid R. Tizhoosh","orcid":"https://orcid.org/0000-0001-5488-601X"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"H. R. Tizhoosh","raw_affiliation_strings":["KIMIA Lab, University of Waterloo, ON, Canada"],"affiliations":[{"raw_affiliation_string":"KIMIA Lab, University of Waterloo, ON, Canada","institution_ids":["https://openalex.org/I151746483"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5077658487"],"corresponding_institution_ids":["https://openalex.org/I151746483"],"apc_list":null,"apc_paid":null,"fwci":10.1846,"has_fulltext":false,"cited_by_count":139,"citation_normalized_percentile":{"value":0.98470567,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9994000196456909,"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":0.9994000196456909,"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/T14510","display_name":"Medical Imaging and Analysis","score":0.9858999848365784,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"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/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.9828000068664551,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7680929899215698},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7578955292701721},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7537239789962769},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7260202169418335},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.6523348093032837},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.6471911668777466},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5677253007888794},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.5674849152565002},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5643495917320251},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5329053997993469},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4865335524082184},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.45114538073539734},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4451587498188019},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3928753733634949},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37868571281433105}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7680929899215698},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7578955292701721},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7537239789962769},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7260202169418335},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.6523348093032837},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.6471911668777466},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5677253007888794},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.5674849152565002},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5643495917320251},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5329053997993469},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4865335524082184},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.45114538073539734},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4451587498188019},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3928753733634949},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37868571281433105},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ipta.2017.8310149","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ipta.2017.8310149","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.44999998807907104,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W1487628423","https://openalex.org/W1516417927","https://openalex.org/W1686810756","https://openalex.org/W1849277567","https://openalex.org/W1992371516","https://openalex.org/W2036924016","https://openalex.org/W2084220915","https://openalex.org/W2101234009","https://openalex.org/W2103243046","https://openalex.org/W2108598243","https://openalex.org/W2112796928","https://openalex.org/W2123237149","https://openalex.org/W2146292423","https://openalex.org/W2153635508","https://openalex.org/W2165698076","https://openalex.org/W2183182206","https://openalex.org/W2183341477","https://openalex.org/W2202393049","https://openalex.org/W2253429366","https://openalex.org/W2346062110","https://openalex.org/W2529590856","https://openalex.org/W2615590620","https://openalex.org/W2689604496","https://openalex.org/W2962835968","https://openalex.org/W2962858109","https://openalex.org/W2963639449","https://openalex.org/W3005347330","https://openalex.org/W3105428393","https://openalex.org/W4285719527","https://openalex.org/W6637373629","https://openalex.org/W6675354045","https://openalex.org/W6678292227","https://openalex.org/W6737615174"],"related_works":["https://openalex.org/W3201126466","https://openalex.org/W3176438653","https://openalex.org/W2981628807","https://openalex.org/W3012393889","https://openalex.org/W3189091156","https://openalex.org/W4386087993","https://openalex.org/W3014041368","https://openalex.org/W4285815841","https://openalex.org/W3193641238","https://openalex.org/W4390143830"],"abstract_inverted_index":{"We":[0,26],"explore":[1],"the":[2,18,42,61,142],"problem":[3],"of":[4,21,44,63,70,82],"classification":[5,137],"within":[6],"a":[7,13,67],"medical":[8],"image":[9],"data-set":[10],"based":[11],"on":[12,76],"feature":[14,29],"vector":[15],"extracted":[16],"from":[17,31,110],"deepest":[19],"layer":[20],"pre-trained":[22,33,45,103],"Convolution":[23],"Neural":[24],"Networks.":[25],"have":[27,51],"used":[28],"vectors":[30],"several":[32],"structures,":[34],"including":[35],"networks":[36,104],"with/without":[37],"transfer":[38,64],"learning":[39,65],"to":[40,118,125],"evaluate":[41],"performance":[43],"deep":[46],"features":[47],"versus":[48],"CNNs":[49],"which":[50,80],"been":[52],"trained":[53],"by":[54],"that":[55,102],"specific":[56],"dataset":[57,79],"as":[58,60],"well":[59],"impact":[62],"with":[66,93],"small":[68],"number":[69],"samples.":[71],"All":[72],"experiments":[73],"are":[74,105],"done":[75],"Kimia":[77],"Path24":[78],"consists":[81],"27,055":[83],"histopathology":[84],"training":[85,109,128],"patches":[86,96],"in":[87,134],"24":[88],"tissue":[89],"texture":[90],"classes":[91],"along":[92],"1,325":[94],"test":[95],"for":[97,123],"evaluation.":[98],"The":[99],"result":[100],"shows":[101],"quite":[106],"competitive":[107],"against":[108],"scratch.":[111],"As":[112],"well,":[113],"fine-tuning":[114],"does":[115],"not":[116],"seem":[117],"add":[119],"any":[120],"tangible":[121],"improvement":[122,133],"VGG16":[124],"justify":[126],"additional":[127],"while":[129],"we":[130,140],"observed":[131],"considerable":[132],"retrieval":[135],"and":[136],"accuracy":[138],"when":[139],"fine-tuned":[141],"Inception":[143],"structure.":[144]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":16},{"year":2023,"cited_by_count":23},{"year":2022,"cited_by_count":20},{"year":2021,"cited_by_count":25},{"year":2020,"cited_by_count":25},{"year":2019,"cited_by_count":18},{"year":2018,"cited_by_count":6}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}