{"id":"https://openalex.org/W7220934634","doi":"https://doi.org/10.2463/mrms.mp.2026-0099","title":"Deep Learning\u2013driven Segmentation of Breast Cancer MR Imaging Using a Hybrid ResNet\u2013Transformer Architecture and Cross-dataset Validation","display_name":"Deep Learning\u2013driven Segmentation of Breast Cancer MR Imaging Using a Hybrid ResNet\u2013Transformer Architecture and Cross-dataset Validation","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7220934634","doi":"https://doi.org/10.2463/mrms.mp.2026-0099","pmid":"https://pubmed.ncbi.nlm.nih.gov/42844165","pmcid":"https://www.ncbi.nlm.nih.gov/pmc/articles/13684323"},"language":"en","primary_location":{"id":"doi:10.2463/mrms.mp.2026-0099","doi":"https://doi.org/10.2463/mrms.mp.2026-0099","is_oa":true,"landing_page_url":"https://doi.org/10.2463/mrms.mp.2026-0099","pdf_url":"https://www.jstage.jst.go.jp/article/mrms/25/4/25_mp.2026-0099/_pdf","source":{"id":"https://openalex.org/S90653425","display_name":"Magnetic Resonance in Medical Sciences","issn_l":"1347-3182","issn":["1347-3182","1880-2206"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","dongbi-c","doyens","jufo-1","medline","oarl","poland-70","russia-white-list-2"],"host_organization":"https://openalex.org/P4310313210","host_organization_name":"Japan Society of Magnetic Resonance in Medicine","host_organization_lineage":["https://openalex.org/P4310313210"],"host_organization_lineage_names":["Japan Society of Magnetic Resonance in Medicine"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Magnetic Resonance in Medical Sciences","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://www.jstage.jst.go.jp/article/mrms/25/4/25_mp.2026-0099/_pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5157954092","display_name":"Yongjian Sui","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yongjian Sui","raw_affiliation_strings":["Department of Radiology, Yongkang First People\u2019s Hospital, Yongkang, Zhejiang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Radiology, Yongkang First People\u2019s Hospital, Yongkang, Zhejiang, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122140953","display_name":"Peiru Li","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peiru Li","raw_affiliation_strings":["Department of Radiology, Yongkang First People\u2019s Hospital, Yongkang, Zhejiang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Radiology, Yongkang First People\u2019s Hospital, Yongkang, Zhejiang, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5157985068","display_name":"Jiacheng Li","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiacheng Li","raw_affiliation_strings":["Department of Radiology, Yongkang First People\u2019s Hospital, Yongkang, Zhejiang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Radiology, Yongkang First People\u2019s Hospital, Yongkang, Zhejiang, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5157941458","display_name":"Peng Zhou","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peng Zhou","raw_affiliation_strings":["Department of Cardiothoracic and Breast Surgery, Yongkang First People\u2019s Hospital, Yongkang, Zhejiang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Cardiothoracic and Breast Surgery, Yongkang First People\u2019s Hospital, Yongkang, Zhejiang, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5157986010","display_name":"Xiaolong Zheng","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Xiaolong Zheng","raw_affiliation_strings":["Department of Radiology, Yongkang First People\u2019s Hospital, Yongkang, Zhejiang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Radiology, Yongkang First People\u2019s Hospital, Yongkang, Zhejiang, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5157986010"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.6861599,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"25","issue":"4","first_page":"n/a","last_page":"n/a"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.8817999958992004,"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"}},"topics":[{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.8817999958992004,"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"}},{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.08839999884366989,"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/T11885","display_name":"MRI in cancer diagnosis","score":0.017400000244379044,"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/breast-cancer","display_name":"breast cancer","score":0.9580000042915344},{"id":"https://openalex.org/keywords/lesion-segmentation","display_name":"lesion segmentation","score":0.5789999961853027},{"id":"https://openalex.org/keywords/dynamic-contrast-enhanced-mri","display_name":"dynamic contrast-enhanced MRI","score":0.9789999723434448},{"id":"https://openalex.org/keywords/u-net","display_name":"U-Net","score":0.9070000052452087},{"id":"https://openalex.org/keywords/cross-dataset-validation","display_name":"cross-dataset validation","score":0.8790000081062317},{"id":"https://openalex.org/keywords/computer-aided-diagnosis","display_name":"computer-aided diagnosis","score":0.6990000009536743},{"id":"https://openalex.org/keywords/data-leakage","display_name":"data leakage","score":0.38100001215934753}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7936999797821045},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7062000036239624},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5828999876976013},{"id":"https://openalex.org/C2777111374","wikidata":"https://www.wikidata.org/wiki/Q4959770","display_name":"Breast MRI","level":5,"score":0.5307000279426575},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.5156000256538391},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.45410001277923584},{"id":"https://openalex.org/C530470458","wikidata":"https://www.wikidata.org/wiki/Q128581","display_name":"Breast cancer","level":3,"score":0.44769999384880066},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4399999976158142},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3874000012874603},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.37869998812675476},{"id":"https://openalex.org/C2779104521","wikidata":"https://www.wikidata.org/wiki/Q23058469","display_name":"Contouring","level":2,"score":0.3684000074863434},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.3644999861717224},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3495999872684479},{"id":"https://openalex.org/C2780472235","wikidata":"https://www.wikidata.org/wiki/Q324634","display_name":"Mammography","level":4,"score":0.3221000134944916},{"id":"https://openalex.org/C163892561","wikidata":"https://www.wikidata.org/wiki/Q2613728","display_name":"S\u00f8rensen\u2013Dice coefficient","level":4,"score":0.30889999866485596},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.3043000102043152},{"id":"https://openalex.org/C2777432617","wikidata":"https://www.wikidata.org/wiki/Q22905905","display_name":"Breast imaging","level":5,"score":0.2980000078678131},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.2897999882698059},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.28610000014305115},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.28360000252723694},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.26980000734329224},{"id":"https://openalex.org/C143409427","wikidata":"https://www.wikidata.org/wiki/Q161238","display_name":"Magnetic resonance imaging","level":2,"score":0.2603999972343445},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.25049999356269836}],"mesh":[{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000098415","descriptor_name":"Convolutional Neural Networks","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000098543","descriptor_name":"Dynamic Contrast Enhanced Magnetic Resonance Imaging","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001943","descriptor_name":"Breast Neoplasms","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":true},{"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":"D007090","descriptor_name":"Image Interpretation, Computer-Assisted","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D007091","descriptor_name":"Image Processing, Computer-Assisted","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D008279","descriptor_name":"Magnetic Resonance Imaging","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D066264","descriptor_name":"Datasets as Topic","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":3,"locations":[{"id":"doi:10.2463/mrms.mp.2026-0099","doi":"https://doi.org/10.2463/mrms.mp.2026-0099","is_oa":true,"landing_page_url":"https://doi.org/10.2463/mrms.mp.2026-0099","pdf_url":"https://www.jstage.jst.go.jp/article/mrms/25/4/25_mp.2026-0099/_pdf","source":{"id":"https://openalex.org/S90653425","display_name":"Magnetic Resonance in Medical Sciences","issn_l":"1347-3182","issn":["1347-3182","1880-2206"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","dongbi-c","doyens","jufo-1","medline","oarl","poland-70","russia-white-list-2"],"host_organization":"https://openalex.org/P4310313210","host_organization_name":"Japan Society of Magnetic Resonance in Medicine","host_organization_lineage":["https://openalex.org/P4310313210"],"host_organization_lineage_names":["Japan Society of Magnetic Resonance in Medicine"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Magnetic Resonance in Medical Sciences","raw_type":"journal-article"},{"id":"pmh:oai:pubmedcentral.nih.gov:13684323","doi":null,"is_oa":true,"landing_page_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13684323/","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13684323/pdf/mrms-25-04-2026-0099.pdf","source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Magn Reson Med Sci","raw_type":"Text"},{"id":"pmid:42844165","doi":null,"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/42844165","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,"listed_in":[],"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":"Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine","raw_type":"Journal Article"}],"best_oa_location":{"id":"doi:10.2463/mrms.mp.2026-0099","doi":"https://doi.org/10.2463/mrms.mp.2026-0099","is_oa":true,"landing_page_url":"https://doi.org/10.2463/mrms.mp.2026-0099","pdf_url":"https://www.jstage.jst.go.jp/article/mrms/25/4/25_mp.2026-0099/_pdf","source":{"id":"https://openalex.org/S90653425","display_name":"Magnetic Resonance in Medical Sciences","issn_l":"1347-3182","issn":["1347-3182","1880-2206"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","dongbi-c","doyens","jufo-1","medline","oarl","poland-70","russia-white-list-2"],"host_organization":"https://openalex.org/P4310313210","host_organization_name":"Japan Society of Magnetic Resonance in Medicine","host_organization_lineage":["https://openalex.org/P4310313210"],"host_organization_lineage_names":["Japan Society of Magnetic Resonance in Medicine"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Magnetic Resonance in Medical Sciences","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.9476000070571899,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7220934634.pdf","grobid_xml":"https://content.openalex.org/works/W7220934634.grobid-xml"},"referenced_works_count":2,"referenced_works":["https://openalex.org/W1836465849","https://openalex.org/W4377864306"],"related_works":["https://openalex.org/W4283514179","https://openalex.org/W4409980484","https://openalex.org/W2953992968","https://openalex.org/W4224278484","https://openalex.org/W3216196611","https://openalex.org/W4401070690","https://openalex.org/W4389887024","https://openalex.org/W3134854716","https://openalex.org/W4414238516","https://openalex.org/W4392642400"],"abstract_inverted_index":{"PURPOSE:":[0],"To":[1],"develop":[2],"and":[3,75,114,171,178,198,246],"cross-dataset":[4,91],"validate":[5],"a":[6,64,68,76,89,165,214],"hybrid":[7,119],"ResNet50-Transformer":[8],"U-Net":[9,65,211],"for":[10,71,80,217,241],"automated":[11,218],"segmentation":[12,123,221],"of":[13,27,167,174],"breast":[14,243],"cancer":[15],"(BC)":[16],"lesions":[17,202],"on":[18,63],"2D":[19],"dynamic":[20],"contrast-enhanced":[21],"MRI":[22,30,35,220,244],"(DCE-MRI).":[23],"METHODS:":[24],"A":[25,46],"total":[26],"20434":[28],"BC":[29,34,219],"images":[31],"from":[32,136,147],"the":[33,100,126,156,161,180,195],"Segmentation":[36,95],"Benchmark":[37],"(BC-MRI-SEG),":[38],"comprising":[39],"4":[40],"independent":[41],"public":[42,224],"datasets,":[43],"were":[44],"included.":[45],"strict":[47],"patient-level":[48,133],"data":[49,56],"splitting":[50],"strategy":[51],"was":[52,61,86,97],"applied":[53],"to":[54,140,151],"avoid":[55],"leakage.":[57],"The":[58,117,208],"proposed":[59,118,162,209],"model":[60,120,163],"based":[62],"architecture":[66],"integrating":[67],"ResNet50":[69],"encoder":[70],"local":[72,231],"feature":[73,232],"extraction":[74,233],"bottleneck":[77],"Transformer":[78],"module":[79],"global":[81,235],"contextual":[82,236],"modeling.":[83],"Model":[84],"performance":[85,96,124],"evaluated":[87,127,181],"using":[88,99],"leave-one-dataset-out":[90,130],"external":[92,131],"validation":[93],"strategy.":[94],"assessed":[98],"Dice":[101],"similarity":[102],"coefficient":[103],"(DSC),":[104],"intersection":[105],"over":[106],"union":[107],"(IoU),":[108],"95%":[109],"Hausdorff":[110],"distance":[111],"(HD95),":[112],"sensitivity,":[113],"precision.":[115],"RESULTS:":[116],"achieved":[121,164],"stable":[122],"across":[125,201,222],"datasets.":[128,225],"In":[129,155],"validation,":[132],"DSC":[134,166],"ranged":[135,146],"0.75":[137],"\u00b1":[138,142,149,153,169,176],"0.13":[139],"0.79":[141],"0.10,":[143],"while":[144],"IoU":[145,173],"0.61":[148],"0.15":[150],"0.66":[152],"0.12.":[154],"fixed":[157],"held-out":[158],"comparison":[159],"cohort,":[160],"0.82":[168],"0.08":[170],"an":[172],"0.70":[175],"0.10":[177],"outperformed":[179],"baseline":[182],"models":[183],"(Holm-adjusted":[184],"P":[185],"\u2264":[186],"0.021).":[187],"Qualitative":[188],"analysis":[189,245],"showed":[190],"generally":[191],"close":[192],"agreement":[193],"between":[194],"predicted":[196],"masks":[197],"reference":[199],"annotations":[200],"with":[203,234],"different":[204],"morphologic":[205],"appearances.":[206],"CONCLUSION:":[207],"ResNet-Transformer-based":[210],"framework":[212],"provided":[213],"robust":[215],"approach":[216],"heterogeneous":[223],"These":[226],"findings":[227],"suggest":[228],"that":[229],"combining":[230],"modeling":[237],"may":[238],"be":[239],"useful":[240],"computer-assisted":[242],"related":[247],"clinical":[248],"workflows.":[249]},"counts_by_year":[],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2026-10-09T00:00:00"}
