{"id":"https://openalex.org/W3200683219","doi":"https://doi.org/10.1109/tip.2021.3112053","title":"Bilateral Asymmetry Guided Counterfactual Generating Network for Mammogram Classification","display_name":"Bilateral Asymmetry Guided Counterfactual Generating Network for Mammogram Classification","publication_year":2020,"publication_date":"2020-09-30","ids":{"openalex":"https://openalex.org/W3200683219","doi":"https://doi.org/10.1109/tip.2021.3112053","mag":"3200683219"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2009.14406","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2009.14406","pdf_url":"https://arxiv.org/pdf/2009.14406","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"article","indexed_in":["arxiv"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2009.14406","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5011041316","display_name":"Churan Wang","orcid":"https://orcid.org/0000-0002-7699-0894"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wang, Chu-ran","raw_affiliation_strings":["Center for Data Science, Peking University, Beijing, 100871, China and Deepwise AI lab"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center for Data Science, Peking University, Beijing, 100871, China and Deepwise AI lab","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100337135","display_name":"Jing Li","orcid":"https://orcid.org/0000-0003-4629-5927"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li, Jing","raw_affiliation_strings":["Dept. of Computer Science, Peking University, Beijing, 100871, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Computer Science, Peking University, Beijing, 100871, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022828476","display_name":"Fandong Zhang","orcid":"https://orcid.org/0000-0003-0655-1180"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhang, Fandong","raw_affiliation_strings":["Center for Data Science, Peking University, Beijing, 100871, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center for Data Science, Peking University, Beijing, 100871, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076341654","display_name":"Xinwei Sun","orcid":"https://orcid.org/0000-0001-6962-7985"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sun, Xinwei","raw_affiliation_strings":["Peking University, Beijing, 100871, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, 100871, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100709867","display_name":"Hao Dong","orcid":"https://orcid.org/0000-0002-7984-9909"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dong, Hao","raw_affiliation_strings":["Center on Frontiers of Computing Studies, Dept. of Computer Science, Peking University, Beijing, 100871, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center on Frontiers of Computing Studies, Dept. of Computer Science, Peking University, Beijing, 100871, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108557359","display_name":"Yizhou Yu","orcid":"https://orcid.org/0000-0002-0470-5548"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Yizhou","raw_affiliation_strings":["Deepwise AI Lab, Beijing, 100080, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Deepwise AI Lab, Beijing, 100080, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100602395","display_name":"Yizhou Wang","orcid":"https://orcid.org/0000-0001-9888-6409"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wang, Yizhou","raw_affiliation_strings":["Dept. of Computer Science, Peking University, Beijing, 100871, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Computer Science, Peking University, Beijing, 100871, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":21,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":1.0,"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":1.0,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9940000176429749,"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"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9779999852180481,"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/counterfactual-thinking","display_name":"Counterfactual thinking","score":0.9741510152816772},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.641821026802063},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6271376013755798},{"id":"https://openalex.org/keywords/lesion","display_name":"Lesion","score":0.4748387932777405},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4668809771537781},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4309411644935608},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4155905544757843},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3762788474559784},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.11414635181427002}],"concepts":[{"id":"https://openalex.org/C108650721","wikidata":"https://www.wikidata.org/wiki/Q1783253","display_name":"Counterfactual thinking","level":2,"score":0.9741510152816772},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.641821026802063},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6271376013755798},{"id":"https://openalex.org/C2781156865","wikidata":"https://www.wikidata.org/wiki/Q827023","display_name":"Lesion","level":2,"score":0.4748387932777405},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4668809771537781},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4309411644935608},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4155905544757843},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3762788474559784},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.11414635181427002},{"id":"https://openalex.org/C118552586","wikidata":"https://www.wikidata.org/wiki/Q7867","display_name":"Psychiatry","level":1,"score":0.0},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:arXiv.org:2009.14406","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2009.14406","pdf_url":"https://arxiv.org/pdf/2009.14406","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2009.14406","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2009.14406","pdf_url":"https://arxiv.org/pdf/2009.14406","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":52,"referenced_works":["https://openalex.org/W304373761","https://openalex.org/W1996591481","https://openalex.org/W2016891892","https://openalex.org/W2070174505","https://openalex.org/W2071997362","https://openalex.org/W2085152177","https://openalex.org/W2086543716","https://openalex.org/W2096733369","https://openalex.org/W2097345597","https://openalex.org/W2099471712","https://openalex.org/W2133059825","https://openalex.org/W2143891888","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2295107390","https://openalex.org/W2527654160","https://openalex.org/W2557738935","https://openalex.org/W2598634450","https://openalex.org/W2599354622","https://openalex.org/W2603777577","https://openalex.org/W2736374171","https://openalex.org/W2770241596","https://openalex.org/W2778924750","https://openalex.org/W2784996692","https://openalex.org/W2884547199","https://openalex.org/W2905307056","https://openalex.org/W2911188335","https://openalex.org/W2912096799","https://openalex.org/W2914298543","https://openalex.org/W2943690666","https://openalex.org/W2949671016","https://openalex.org/W2951225967","https://openalex.org/W2955425717","https://openalex.org/W2962793481","https://openalex.org/W2963446712","https://openalex.org/W2963811535","https://openalex.org/W2963981733","https://openalex.org/W2964166828","https://openalex.org/W2964189045","https://openalex.org/W2964275459","https://openalex.org/W2979729779","https://openalex.org/W2980030301","https://openalex.org/W2981988113","https://openalex.org/W2995191098","https://openalex.org/W3000237049","https://openalex.org/W3035398999","https://openalex.org/W3091674217","https://openalex.org/W3099206234","https://openalex.org/W3104943862","https://openalex.org/W4300485340","https://openalex.org/W4301206121","https://openalex.org/W4320013936"],"related_works":["https://openalex.org/W3201448254","https://openalex.org/W4286970243","https://openalex.org/W2066431708","https://openalex.org/W4384133558","https://openalex.org/W3025615835","https://openalex.org/W173210993","https://openalex.org/W2390660599","https://openalex.org/W3028847759","https://openalex.org/W2393688264","https://openalex.org/W3170174360"],"abstract_inverted_index":{"Mammogram":[0],"benign":[1],"or":[2],"malignant":[3],"classification":[4,149,172],"with":[5],"only":[6],"image-level":[7],"labels":[8],"is":[9,122],"challenging":[10],"due":[11],"to":[12,68,155],"the":[13,20,24,34,38,54,64,70,84,143,148,160,163,169,180],"absence":[14],"of":[15,29,125,171,176,182],"lesion":[16,71,157],"annotations.":[17],"Motivated":[18],"by":[19,151,168,191],"symmetric":[21,85],"prior":[22,96],"that":[23,51,92],"lesions":[25,62],"on":[26,37,83,194],"one":[27],"side":[28],"breasts":[30],"rarely":[31],"appear":[32],"in":[33,63,185],"corresponding":[35],"areas":[36],"other":[39,161],"side,":[40],"given":[41],"a":[42,48,75,89,95,130],"diseased":[43],"image,":[44,65],"we":[45,100],"can":[46,109,145,188],"explore":[47],"counterfactual":[49,80,106,116,153,166],"problem":[50],"how":[52],"would":[53],"features":[55,154],"have":[56],"behaved":[57],"if":[58],"there":[59],"were":[60],"no":[61],"so":[66],"as":[67],"identify":[69],"areas.":[72,158],"We":[73],"derive":[74],"new":[76],"theoretical":[77],"result":[78],"for":[79,97,105],"generation":[81,167],"based":[82],"prior.":[86],"By":[87],"building":[88],"causal":[90],"model":[91,121,187],"entails":[93],"such":[94],"bilateral":[98],"images,":[99],"obtain":[101],"two":[102],"optimization":[103],"goals":[104],"generation,":[107],"which":[108],"be":[110,189],"accomplished":[111],"via":[112],"our":[113,177,186],"newly":[114],"proposed":[115,120],"generative":[117],"network.":[118],"Our":[119],"mainly":[123],"composed":[124],"Generator":[126],"Adversarial":[127],"Network":[128],"and":[129,138,179,196,200],"\\emph{prediction":[131],"feedback":[132],"mechanism},":[133],"they":[134],"are":[135],"optimized":[136],"jointly":[137],"prompt":[139],"each":[140,183],"other.":[141],"Specifically,":[142],"former":[144],"further":[146],"improve":[147],"performance":[150,193],"generating":[152],"calculate":[156],"On":[159],"hand,":[162],"latter":[164],"helps":[165],"supervision":[170],"loss.":[173],"The":[174],"utility":[175],"method":[178],"effectiveness":[181],"module":[184],"verified":[190],"state-of-the-art":[192],"INBreast":[195],"an":[197],"in-house":[198],"dataset":[199],"ablation":[201],"studies.":[202]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":3}],"updated_date":"2026-07-03T08:13:44.112507","created_date":"2025-10-10T00:00:00"}