{"id":"https://openalex.org/W3009175488","doi":"https://doi.org/10.1109/tcbb.2020.2978470","title":"Lesion Segmentation in Ultrasound Using Semi-Pixel-Wise Cycle Generative Adversarial Nets","display_name":"Lesion Segmentation in Ultrasound Using Semi-Pixel-Wise Cycle Generative Adversarial Nets","publication_year":2020,"publication_date":"2020-03-06","ids":{"openalex":"https://openalex.org/W3009175488","doi":"https://doi.org/10.1109/tcbb.2020.2978470","mag":"3009175488","pmid":"https://pubmed.ncbi.nlm.nih.gov/32149651"},"language":"en","primary_location":{"id":"doi:10.1109/tcbb.2020.2978470","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcbb.2020.2978470","pdf_url":null,"source":{"id":"https://openalex.org/S36029991","display_name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","issn_l":"1545-5963","issn":["1545-5963","1557-9964","2374-0043"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://research.tue.nl/en/publications/cbd9783a-dc08-476f-9d58-592d38cea38b","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5064455846","display_name":"Jie Xing","orcid":"https://orcid.org/0000-0002-5689-9268"},"institutions":[{"id":"https://openalex.org/I4210098734","display_name":"Zhejiang Cancer Hospital","ror":"https://ror.org/0144s0951","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210098734"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jie Xing","raw_affiliation_strings":["Zhejiang Cancer Hospital, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-5689-9268","affiliations":[{"raw_affiliation_string":"Zhejiang Cancer Hospital, Hangzhou, China","institution_ids":["https://openalex.org/I4210098734"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032615365","display_name":"Zheren Li","orcid":"https://orcid.org/0000-0003-3787-3940"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zheren Li","raw_affiliation_strings":["School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0003-3787-3940","affiliations":[{"raw_affiliation_string":"School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107946933","display_name":"Biyuan Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I114531698","display_name":"Tokyo Institute of Technology","ror":"https://ror.org/0112mx960","country_code":"JP","type":"education","lineage":["https://openalex.org/I114531698"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Biyuan Wang","raw_affiliation_strings":["Department of Computing, Tokyo Institute of Technology, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computing, Tokyo Institute of Technology, Tokyo, Japan","institution_ids":["https://openalex.org/I114531698"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055534880","display_name":"Yuji Qi","orcid":"https://orcid.org/0000-0002-0571-8463"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuji Qi","raw_affiliation_strings":["Department of Biomedical Engineering, Yale University, New Haven, CT, USA"],"raw_orcid":"https://orcid.org/0000-0002-0571-8463","affiliations":[{"raw_affiliation_string":"Department of Biomedical Engineering, Yale University, New Haven, CT, USA","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076068721","display_name":"Bingbin Yu","orcid":"https://orcid.org/0000-0001-5400-0880"},"institutions":[{"id":"https://openalex.org/I33256026","display_name":"German Research Centre for Artificial Intelligence","ror":"https://ror.org/01ayc5b57","country_code":"DE","type":"funder","lineage":["https://openalex.org/I33256026"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Bingbin Yu","raw_affiliation_strings":["Robotic Innovation Center, German Research Center of Artificial Intelligence, Bremen, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Robotic Innovation Center, German Research Center of Artificial Intelligence, Bremen, Germany","institution_ids":["https://openalex.org/I33256026"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060435886","display_name":"Farhad Ghazvinian Zanjani","orcid":"https://orcid.org/0000-0002-9625-5563"},"institutions":[{"id":"https://openalex.org/I83019370","display_name":"Eindhoven University of Technology","ror":"https://ror.org/02c2kyt77","country_code":"NL","type":"education","lineage":["https://openalex.org/I83019370"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Farhad Ghazvinian Zanjani","raw_affiliation_strings":["Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven 5600 MB, The Netherlands"],"raw_orcid":"https://orcid.org/0000-0002-9625-5563","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven 5600 MB, The Netherlands","institution_ids":["https://openalex.org/I83019370"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030051269","display_name":"Aiwen Zheng","orcid":null},"institutions":[{"id":"https://openalex.org/I4210098734","display_name":"Zhejiang Cancer Hospital","ror":"https://ror.org/0144s0951","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210098734"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Aiwen Zheng","raw_affiliation_strings":["Zhejiang Cancer Hospital, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang Cancer Hospital, Hangzhou, China","institution_ids":["https://openalex.org/I4210098734"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013378425","display_name":"Remco Duits","orcid":"https://orcid.org/0000-0001-8910-3695"},"institutions":[{"id":"https://openalex.org/I83019370","display_name":"Eindhoven University of Technology","ror":"https://ror.org/02c2kyt77","country_code":"NL","type":"education","lineage":["https://openalex.org/I83019370"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Remco Duits","raw_affiliation_strings":["Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven 5600 MB, The Netherlands"],"raw_orcid":"https://orcid.org/0000-0001-8910-3695","affiliations":[{"raw_affiliation_string":"Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven 5600 MB, The Netherlands","institution_ids":["https://openalex.org/I83019370"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101628586","display_name":"Tao Tan","orcid":"https://orcid.org/0000-0002-5573-1105"},"institutions":[{"id":"https://openalex.org/I83019370","display_name":"Eindhoven University of Technology","ror":"https://ror.org/02c2kyt77","country_code":"NL","type":"education","lineage":["https://openalex.org/I83019370"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Tao Tan","raw_affiliation_strings":["Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven 5600 MB, The Netherlands"],"raw_orcid":"https://orcid.org/0000-0002-5573-1105","affiliations":[{"raw_affiliation_string":"Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven 5600 MB, The Netherlands","institution_ids":["https://openalex.org/I83019370"]}]}],"institutions":[],"countries_distinct_count":5,"institutions_distinct_count":9,"corresponding_author_ids":["https://openalex.org/A5064455846"],"corresponding_institution_ids":["https://openalex.org/I4210098734"],"apc_list":null,"apc_paid":null,"fwci":3.805,"has_fulltext":false,"cited_by_count":44,"citation_normalized_percentile":{"value":0.94448389,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"18","issue":"6","first_page":"2555","last_page":"2565"},"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":0.9969000220298767,"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":0.9897000193595886,"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/segmentation","display_name":"Segmentation","score":0.7313856482505798},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7270172834396362},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6655067205429077},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6318190693855286},{"id":"https://openalex.org/keywords/s\u00f8rensen\u2013dice-coefficient","display_name":"S\u00f8rensen\u2013Dice coefficient","score":0.6121423244476318},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5823302865028381},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5691348314285278},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5263948440551758},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5068543553352356},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.4217730164527893},{"id":"https://openalex.org/keywords/ultrasound","display_name":"Ultrasound","score":0.4138435423374176},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.37479451298713684},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.277417927980423},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.23288586735725403}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7313856482505798},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7270172834396362},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6655067205429077},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6318190693855286},{"id":"https://openalex.org/C163892561","wikidata":"https://www.wikidata.org/wiki/Q2613728","display_name":"S\u00f8rensen\u2013Dice coefficient","level":4,"score":0.6121423244476318},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5823302865028381},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5691348314285278},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5263948440551758},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5068543553352356},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.4217730164527893},{"id":"https://openalex.org/C143753070","wikidata":"https://www.wikidata.org/wiki/Q162564","display_name":"Ultrasound","level":2,"score":0.4138435423374176},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.37479451298713684},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.277417927980423},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.23288586735725403}],"mesh":[{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001940","descriptor_name":"Breast","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":false},{"descriptor_ui":"D001940","descriptor_name":"Breast","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":false},{"descriptor_ui":"D001940","descriptor_name":"Breast","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":false},{"descriptor_ui":"D001943","descriptor_name":"Breast Neoplasms","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":false},{"descriptor_ui":"D001943","descriptor_name":"Breast Neoplasms","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":false},{"descriptor_ui":"D001943","descriptor_name":"Breast Neoplasms","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":false},{"descriptor_ui":"D005260","descriptor_name":"Female","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005260","descriptor_name":"Female","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005260","descriptor_name":"Female","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D007090","descriptor_name":"Image Interpretation, Computer-Assisted","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D007090","descriptor_name":"Image Interpretation, Computer-Assisted","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D007090","descriptor_name":"Image Interpretation, Computer-Assisted","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D016217","descriptor_name":"Ultrasonography, Mammary","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D016217","descriptor_name":"Ultrasonography, Mammary","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D016217","descriptor_name":"Ultrasonography, Mammary","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false}],"locations_count":6,"locations":[{"id":"doi:10.1109/tcbb.2020.2978470","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcbb.2020.2978470","pdf_url":null,"source":{"id":"https://openalex.org/S36029991","display_name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","issn_l":"1545-5963","issn":["1545-5963","1557-9964","2374-0043"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","raw_type":"journal-article"},{"id":"pmid:32149651","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/32149651","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE/ACM transactions on computational biology and bioinformatics","raw_type":null},{"id":"pmh:oai:pure.tue.nl:openaire/cbd9783a-dc08-476f-9d58-592d38cea38b","is_oa":true,"landing_page_url":"https://research.tue.nl/en/publications/cbd9783a-dc08-476f-9d58-592d38cea38b","pdf_url":null,"source":{"id":"https://openalex.org/S4406922641","display_name":"TU/e Research Portal","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Xing, J, Li, Z, Wang, B, Qi, Y, Yu, B, Ghazvinian Zanjani, F, Zheng, A, Duits, R & Tan, T 2021, 'Lesion Segmentation in Ultrasound Using Semi-pixel-wise Cycle Generative Adversarial Nets', IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 18, no. 6, pp. 2555-2565. https://doi.org/10.1109/TCBB.2020.2978470","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:oai:arXiv.org:1905.01902","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1905.01902","pdf_url":"https://arxiv.org/pdf/1905.01902","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"},{"id":"pmh:oai:pure.tue.nl:publications/cbd9783a-dc08-476f-9d58-592d38cea38b","is_oa":true,"landing_page_url":"https://arxiv.org/abs/1905.01902","pdf_url":null,"source":{"id":"https://openalex.org/S4406922641","display_name":"TU/e Research Portal","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Xing, J, Li, Z, Wang, B, Qi, Y, Yu, B, Ghazvinian Zanjani, F, Zheng, A, Duits, R & Tan, T 2021, 'Lesion Segmentation in Ultrasound Using Semi-pixel-wise Cycle Generative Adversarial Nets', IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 18, no. 6, pp. 2555-2565. https://doi.org/10.1109/TCBB.2020.2978470","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:tue:oai:pure.tue.nl:publications/cbd9783a-dc08-476f-9d58-592d38cea38b","is_oa":true,"landing_page_url":"https://research.tue.nl/nl/publications/cbd9783a-dc08-476f-9d58-592d38cea38b","pdf_url":null,"source":{"id":"https://openalex.org/S4306401843","display_name":"Data Archiving and Networked Services (DANS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1322597698","host_organization_name":"Royal Netherlands Academy of Arts and Sciences","host_organization_lineage":["https://openalex.org/I1322597698"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics, 18(6), 2555 - 2565. Institute of Electrical and Electronics Engineers","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"pmh:oai:pure.tue.nl:openaire/cbd9783a-dc08-476f-9d58-592d38cea38b","is_oa":true,"landing_page_url":"https://research.tue.nl/en/publications/cbd9783a-dc08-476f-9d58-592d38cea38b","pdf_url":null,"source":{"id":"https://openalex.org/S4406922641","display_name":"TU/e Research Portal","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Xing, J, Li, Z, Wang, B, Qi, Y, Yu, B, Ghazvinian Zanjani, F, Zheng, A, Duits, R & Tan, T 2021, 'Lesion Segmentation in Ultrasound Using Semi-pixel-wise Cycle Generative Adversarial Nets', IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 18, no. 6, pp. 2555-2565. https://doi.org/10.1109/TCBB.2020.2978470","raw_type":"info:eu-repo/semantics/publishedVersion"},"sustainable_development_goals":[{"score":0.6899999976158142,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":59,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1836465849","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1970091020","https://openalex.org/W1971472203","https://openalex.org/W1978083511","https://openalex.org/W2002625329","https://openalex.org/W2018731410","https://openalex.org/W2041277244","https://openalex.org/W2054341754","https://openalex.org/W2084922904","https://openalex.org/W2086543716","https://openalex.org/W2087998819","https://openalex.org/W2108908829","https://openalex.org/W2121561566","https://openalex.org/W2194775991","https://openalex.org/W2281375744","https://openalex.org/W2292862470","https://openalex.org/W2302255633","https://openalex.org/W2305839233","https://openalex.org/W2404618390","https://openalex.org/W2416559807","https://openalex.org/W2511730936","https://openalex.org/W2572697783","https://openalex.org/W2606179652","https://openalex.org/W2615443777","https://openalex.org/W2618530766","https://openalex.org/W2620296437","https://openalex.org/W2620487580","https://openalex.org/W2640545234","https://openalex.org/W2700917049","https://openalex.org/W2744692634","https://openalex.org/W2770670552","https://openalex.org/W2776559369","https://openalex.org/W2777741489","https://openalex.org/W2790645305","https://openalex.org/W2793492234","https://openalex.org/W2804525729","https://openalex.org/W2905711806","https://openalex.org/W2950906573","https://openalex.org/W2962793481","https://openalex.org/W2963150697","https://openalex.org/W2963343946","https://openalex.org/W2963446712","https://openalex.org/W2963493912","https://openalex.org/W2964121744","https://openalex.org/W3211330693","https://openalex.org/W4285719527","https://openalex.org/W4308909683","https://openalex.org/W4320013936","https://openalex.org/W4394669457","https://openalex.org/W6631190155","https://openalex.org/W6638667902","https://openalex.org/W6639824700","https://openalex.org/W6687483927","https://openalex.org/W6697921425","https://openalex.org/W6698183232","https://openalex.org/W6725739302"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W2085033728","https://openalex.org/W4285411112","https://openalex.org/W2171299904","https://openalex.org/W1647606319","https://openalex.org/W4390494008","https://openalex.org/W2053596378","https://openalex.org/W2922442631","https://openalex.org/W2168523118","https://openalex.org/W4283772513"],"abstract_inverted_index":{"Breast":[0],"cancer":[1,7,11],"is":[2,17,40,184],"the":[3,9,20,28,34,66,74,98,108,140,155,159,205],"most":[4,21],"common":[5],"invasive":[6],"with":[8],"highest":[10],"occurrence":[12],"in":[13,68],"females.":[14],"Handheld":[15],"ultrasound":[16],"one":[18],"of":[19,37,76,118,135,158,182],"efficient":[22],"ways":[23],"to":[24,45,88,101,164,195,203],"identify":[25],"and":[26,33,83,107,122,139,145],"diagnose":[27],"breast":[29],"cancer.":[30],"The":[31,71,169,180],"area":[32],"shape":[35],"information":[36],"a":[38,54,77,84,90,102,114,130],"lesion":[39,67,91],"very":[41],"helpful":[42],"for":[43,64,149,208],"clinicians":[44],"make":[46],"diagnostic":[47],"decisions.":[48],"In":[49],"this":[50],"study":[51],"we":[52],"propose":[53],"new":[55],"deep-learning":[56],"scheme,":[57],"semi-pixel-wise":[58],"cycle":[59],"generative":[60,85],"adversarial":[61,86],"net":[62,87],"(SPCGAN)":[63],"segmenting":[65],"2D":[69],"ultrasound.":[70],"method":[72,100,112,128,153,199],"takes":[73],"advantage":[75],"fully":[78,103,160],"convolutional":[79],"neural":[80,105,162],"network":[81,106,163],"(FCN)":[82],"segment":[89],"by":[92],"using":[93],"prior":[94],"knowledge.":[95],"We":[96],"compared":[97,194],"proposed":[99,127,198],"connected":[104,161],"level":[109,141],"set":[110,142],"segmentation":[111,178],"on":[113],"test":[115],"dataset":[116],"consisting":[117],"32":[119],"malignant":[120,150],"lesions":[121],"109":[123],"benign":[124],"lesions.":[125],"Our":[126,197],"achieved":[129,143],"Dice":[131],"similarity":[132],"coefficient":[133],"(DSC)":[134],"0.92":[136],"while":[137],"FCN":[138],"0.90":[144],"0.79":[146],"respectively.":[147],"Particularly,":[148],"lesions,":[151],"our":[152,173],"increases":[154],"DSC":[156],"(0.90)":[157],"0.93":[165],"significantly":[166],"(p":[167],"0.001).":[168],"results":[170],"show":[171],"that":[172],"SPCGAN":[174,183],"can":[175],"obtain":[176],"robust":[177],"results.":[179],"framework":[181],"particularly":[185],"effective":[186],"when":[187],"sufficient":[188],"training":[189],"samples":[190],"are":[191],"not":[192],"available":[193],"FCN.":[196],"may":[200],"be":[201],"used":[202],"relieve":[204],"radiologists'":[206],"burden":[207],"annotation.":[209]},"counts_by_year":[{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}