{"id":"https://openalex.org/W7162125593","doi":"https://doi.org/10.48550/arxiv.2605.22327","title":"Robustness of breast lesion segmentation under MRI undersampling improves with k-space-aware deep learning","display_name":"Robustness of breast lesion segmentation under MRI undersampling improves with k-space-aware deep learning","publication_year":2026,"publication_date":"2026-05-21","ids":{"openalex":"https://openalex.org/W7162125593","doi":"https://doi.org/10.48550/arxiv.2605.22327"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.22327","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.22327","pdf_url":null,"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.22327","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5123973244","display_name":"Lukas T. Rotkopf","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rotkopf, Lukas T.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123904391","display_name":"Marco Schlimbach","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Schlimbach, Marco","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136807293","display_name":"Julius C. Holzschuh","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Holzschuh, Julius C.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136784857","display_name":"Heinz-Peter Schlemmer","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Schlemmer, Heinz-Peter","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136735181","display_name":"Jens Kleesiek","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kleesiek, Jens","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136774524","display_name":"Moritz Rempe","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rempe, Moritz","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"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/T11885","display_name":"MRI in cancer diagnosis","score":0.9740999937057495,"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"}},"topics":[{"id":"https://openalex.org/T11885","display_name":"MRI in cancer diagnosis","score":0.9740999937057495,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.007300000172108412,"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/T10378","display_name":"Advanced MRI Techniques and Applications","score":0.00430000014603138,"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/undersampling","display_name":"Undersampling","score":0.9524000287055969},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7203999757766724},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6690000295639038},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6281999945640564},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5649999976158142},{"id":"https://openalex.org/keywords/breast-mri","display_name":"Breast MRI","score":0.4198000133037567},{"id":"https://openalex.org/keywords/real-time-mri","display_name":"Real-time MRI","score":0.39430001378059387}],"concepts":[{"id":"https://openalex.org/C136536468","wikidata":"https://www.wikidata.org/wiki/Q1225894","display_name":"Undersampling","level":2,"score":0.9524000287055969},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7203999757766724},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7161999940872192},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6690000295639038},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6281999945640564},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5884000062942505},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5649999976158142},{"id":"https://openalex.org/C2777111374","wikidata":"https://www.wikidata.org/wiki/Q4959770","display_name":"Breast MRI","level":5,"score":0.4198000133037567},{"id":"https://openalex.org/C157787499","wikidata":"https://www.wikidata.org/wiki/Q13479657","display_name":"Real-time MRI","level":3,"score":0.39430001378059387},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.3138999938964844},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.30889999866485596},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.3001999855041504},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.29760000109672546},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.28870001435279846},{"id":"https://openalex.org/C2779098232","wikidata":"https://www.wikidata.org/wiki/Q903975","display_name":"BI-RADS","level":5,"score":0.2833999991416931},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.27379998564720154},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.2712000012397766},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2653000056743622},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.26100000739097595}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.22327","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.22327","pdf_url":null,"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.22327","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.22327","pdf_url":null,"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Purpose:":[0],"To":[1],"assess":[2],"whether":[3,16],"breast":[4,35,207],"lesion":[5,197,208],"segmentation":[6,128,209],"can":[7],"be":[8],"learned":[9],"directly":[10,152],"from":[11],"acquired":[12,42],"MRI":[13,38,211],"k-space,":[14,45],"and":[15,28,43,66,68,78,114,130,185,213],"doing":[17],"so":[18],"improves":[19,203],"robustness":[20,205],"when":[21,148],"data":[22],"are":[23],"accelerated":[24],"or":[25],"noisy.":[26],"Materials":[27],"Methods:":[29],"This":[30,169],"retrospective":[31],"study":[32],"used":[33],"public":[34],"dynamic":[36],"contrast-enhanced":[37],"(DCE-MRI)":[39],"datasets":[40],"with":[41,47,94,191],"synthetic":[44,50,176],"together":[46],"a":[48,58,62],"within-dataset":[49,175],"control.":[51,177],"We":[52],"compared":[53],"four":[54],"3D":[55],"U-Net":[56],"variants:":[57],"hybrid":[59,96,113,123,156],"k-space-to-image":[60],"model,":[61,65],"native":[63],"k-space":[64,82,183,214],"magnitude":[67,105,134],"complex":[69,80],"image-space":[70,106,115,135,163,186,218],"baselines.":[71],"Models":[72],"were":[73],"evaluated":[74],"under":[75,92,166,210],"increasing":[76],"undersampling":[77,141,212],"added":[79,151],"Gaussian":[81],"noise.":[83,168],"The":[84,143],"primary":[85],"outcome":[86],"was":[87,146,150,171],"patient-level":[88],"Dice":[89],"similarity":[90],"coefficient":[91],"cross-validation,":[93],"the":[95,100,104,112,122,133,155,162,174,182,204],"model":[97,124,157],"prespecified":[98],"as":[99],"main":[101],"comparison":[102],"against":[103],"baseline.":[107],"Results:":[108],"At":[109],"full":[110,221],"sampling,":[111],"models":[116],"performed":[117],"similarly.":[118],"As":[119],"acceleration":[120],"increased,":[121],"retained":[125],"substantially":[126],"more":[127,159],"accuracy":[129],"significantly":[131],"outperformed":[132],"baseline":[136,164],"across":[137],"moderate":[138],"to":[139,153],"high":[140],"levels.":[142],"same":[144],"pattern":[145],"observed":[147],"noise":[149],"k-space:":[154],"degraded":[158],"slowly,":[160],"whereas":[161],"failed":[165],"heavier":[167],"advantage":[170],"reproduced":[172],"in":[173],"Feature":[178],"analysis":[179],"suggested":[180],"that":[181],"stage":[184,187],"played":[188],"complementary":[189],"roles,":[190],"frequency-domain":[192],"filtering":[193],"concentrated":[194],"before":[195],"image-domain":[196],"localization.":[198],"Conclusion:":[199],"K-space-aware":[200],"deep":[201],"learning":[202],"of":[206],"noise,":[215],"while":[216],"matching":[217],"methods":[219],"at":[220],"sampling.":[222]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-23T00:00:00"}