{"id":"https://openalex.org/W3109954266","doi":"https://doi.org/10.1109/cisp-bmei51763.2020.9263676","title":"Boundary Loss with Non-Euclidean Distance Constraint for ABUS Mass Segmentation","display_name":"Boundary Loss with Non-Euclidean Distance Constraint for ABUS Mass Segmentation","publication_year":2020,"publication_date":"2020-10-17","ids":{"openalex":"https://openalex.org/W3109954266","doi":"https://doi.org/10.1109/cisp-bmei51763.2020.9263676","mag":"3109954266"},"language":"en","primary_location":{"id":"doi:10.1109/cisp-bmei51763.2020.9263676","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cisp-bmei51763.2020.9263676","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","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/A5067159903","display_name":"Xuyang Cao","orcid":"https://orcid.org/0000-0001-9835-7742"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xuyang Cao","raw_affiliation_strings":["Beijing Jiaotong University,School of Electronic and Information Engineering,Beijing,China","School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University,School of Electronic and Information Engineering,Beijing,China","institution_ids":["https://openalex.org/I21193070"]},{"raw_affiliation_string":"School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073025681","display_name":"Houjin Chen","orcid":"https://orcid.org/0000-0002-9247-8495"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Houjin Chen","raw_affiliation_strings":["Beijing Jiaotong University,School of Electronic and Information Engineering,Beijing,China","School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University,School of Electronic and Information Engineering,Beijing,China","institution_ids":["https://openalex.org/I21193070"]},{"raw_affiliation_string":"School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100653133","display_name":"Yanfeng Li","orcid":"https://orcid.org/0000-0002-8441-7721"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanfeng Li","raw_affiliation_strings":["Beijing Jiaotong University,School of Electronic and Information Engineering,Beijing,China","School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University,School of Electronic and Information Engineering,Beijing,China","institution_ids":["https://openalex.org/I21193070"]},{"raw_affiliation_string":"School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089537405","display_name":"Yahui Peng","orcid":"https://orcid.org/0000-0002-2520-1170"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yahui Peng","raw_affiliation_strings":["Beijing Jiaotong University,School of Electronic and Information Engineering,Beijing,China","School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University,School of Electronic and Information Engineering,Beijing,China","institution_ids":["https://openalex.org/I21193070"]},{"raw_affiliation_string":"School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101632405","display_name":"Yue Zhou","orcid":"https://orcid.org/0000-0003-4859-8016"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yue Zhou","raw_affiliation_strings":["Beijing Jiaotong University,School of Electronic and Information Engineering,Beijing,China","School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University,School of Electronic and Information Engineering,Beijing,China","institution_ids":["https://openalex.org/I21193070"]},{"raw_affiliation_string":"School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":null,"display_name":"Lin Cheng","orcid":null},"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"]},{"id":"https://openalex.org/I4210124809","display_name":"Peking University People's Hospital","ror":"https://ror.org/035adwg89","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210124809"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lin Cheng","raw_affiliation_strings":["Peking University People\u2019s Hospital,Breast Center,Beijing,China","Breast Center, Peking University People's Hospital, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Peking University People\u2019s Hospital,Breast Center,Beijing,China","institution_ids":["https://openalex.org/I4210124809","https://openalex.org/I20231570"]},{"raw_affiliation_string":"Breast Center, Peking University People's Hospital, Beijing, China","institution_ids":["https://openalex.org/I4210124809","https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5067159903"],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":null,"apc_paid":null,"fwci":0.2718,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.65958634,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"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":0.9998999834060669,"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":0.9998999834060669,"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":0.9948999881744385,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9922000169754028,"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":0.7451313138008118},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6978409886360168},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6470195055007935},{"id":"https://openalex.org/keywords/euclidean-distance","display_name":"Euclidean distance","score":0.631056010723114},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5593996047973633},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5237603187561035},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.46393588185310364},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.462485134601593},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4244096875190735},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.41291502118110657},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4073992371559143},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.186438649892807}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7451313138008118},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6978409886360168},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6470195055007935},{"id":"https://openalex.org/C120174047","wikidata":"https://www.wikidata.org/wiki/Q847073","display_name":"Euclidean distance","level":2,"score":0.631056010723114},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5593996047973633},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5237603187561035},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.46393588185310364},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.462485134601593},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4244096875190735},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.41291502118110657},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4073992371559143},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.186438649892807},{"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/cisp-bmei51763.2020.9263676","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cisp-bmei51763.2020.9263676","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1555845716","https://openalex.org/W2059975159","https://openalex.org/W2194775991","https://openalex.org/W2341856765","https://openalex.org/W2416559807","https://openalex.org/W2464708700","https://openalex.org/W2527854671","https://openalex.org/W2770670552","https://openalex.org/W2776559369","https://openalex.org/W2796329624","https://openalex.org/W2884034690","https://openalex.org/W2884561390","https://openalex.org/W2889646458","https://openalex.org/W2899332989","https://openalex.org/W2923997689","https://openalex.org/W2962914239","https://openalex.org/W2963442459","https://openalex.org/W2964098128","https://openalex.org/W2966434031","https://openalex.org/W3015130244","https://openalex.org/W3094183857","https://openalex.org/W6766318054"],"related_works":["https://openalex.org/W1986655823","https://openalex.org/W2185902295","https://openalex.org/W2103507220","https://openalex.org/W2019538911","https://openalex.org/W3144569342","https://openalex.org/W3011384228","https://openalex.org/W2124969951","https://openalex.org/W2467200550","https://openalex.org/W1996805379","https://openalex.org/W2945274617"],"abstract_inverted_index":{"Mass":[0],"segmentation":[1,23,63],"plays":[2],"an":[3],"important":[4],"role":[5],"in":[6,49],"the":[7,30,41,68,89,130,133,152,156,159],"qualitative":[8],"and":[9,40,47,93,100,120],"quantitative":[10],"analysis":[11],"of":[12,132,147,158],"3D":[13,21,60,73,163],"automated":[14],"breast":[15],"ultrasound":[16],"(ABUS)":[17],"volumes.":[18,51,125],"However,":[19],"accurate":[20],"mass":[22,165],"is":[24,76,113,118,136],"a":[25,57,95,104,108,142],"challenging":[26],"task":[27],"due":[28],"to":[29,33,66,78,87],"low":[31],"signal":[32],"noise":[34],"ratio,":[35],"large":[36],"tumor":[37,62],"size":[38],"variations,":[39],"serious":[42],"class":[43,69,90],"imbalance":[44,70,91],"between":[45,97],"foreground":[46],"background":[48],"ABUS":[50,61,124,164],"In":[52,85],"this":[53],"paper,":[54],"we":[55],"present":[56],"deep":[58],"learning-based":[59],"method":[64,117,135,161],"mainly":[65],"solve":[67,88],"problem.":[71],"A":[72],"Residual":[74],"U-Net":[75],"designed":[77],"effectively":[79],"explore":[80],"feature":[81],"representations":[82],"during":[83],"training.":[84],"order":[86],"problem":[92],"make":[94],"trade-off":[96],"false":[98,101],"positive":[99],"negative":[102],"predictions,":[103],"boundary":[105],"loss":[106],"with":[107,141],"signed":[109],"non-Euclidean":[110],"distance":[111],"map":[112],"introduced.":[114],"The":[115],"proposed":[116,134,160],"trained":[119],"evaluated":[121],"on":[122,151,162],"83":[123],"Experimental":[126],"results":[127],"show":[128],"that":[129],"performance":[131],"better":[137],"than":[138],"existing":[139],"methods,":[140],"Dice":[143],"similarity":[144],"coefficient":[145],"(DSC)":[146],"0.82":[148],"\u00b1":[149],"0.08":[150],"testing":[153],"set,":[154],"indicating":[155],"effectiveness":[157],"segmentation.":[166]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-04-16T08:26:57.006410","created_date":"2025-10-10T00:00:00"}
