2025
Using Foundation Models as Pseudo-label Generators for Pre-clinical 4D Cardiac CT Segmentation
Rickmann A, Thorn S, Ahn S, Lee S, Uman S, Lysyy T, Burns R, Guerrera N, Spinale F, Burdick J, Sinusas A, Duncan J. Using Foundation Models as Pseudo-label Generators for Pre-clinical 4D Cardiac CT Segmentation. Lecture Notes In Computer Science 2025, 15673: 253-265. DOI: 10.1007/978-3-031-94562-5_23.Peer-Reviewed Original ResearchImage segmentationRobust Medical Image SegmentationAccurate pseudo labelsPseudo-label generationSelf-training strategySelf-training approachMedical image segmentationEnhance segmentation accuracyCardiac image segmentationImprove segmentation qualitySelf-training processPseudo-labelsDomain shiftConsecutive framesCardiac image analysisDeep learningSegmentation qualitySegmentation accuracyModeling tasksIterative updateTemporal inconsistencyMotion trackingCT segmentationHuman datasetsImage analysis
2024
Exploring Backdoor Attacks in Off-the-Shelf Unsupervised Domain Adaptation for Securing Cardiac MRI-Based Diagnosis
Liu X, Xing F, Gaggin H, Kuo C, El Fakhri G, Woo J. Exploring Backdoor Attacks in Off-the-Shelf Unsupervised Domain Adaptation for Securing Cardiac MRI-Based Diagnosis. 2011 IEEE International Symposium On Biomedical Imaging: From Nano To Macro 2024, 00: 1-5. PMID: 39421190, PMCID: PMC11483644, DOI: 10.1109/isbi56570.2024.10635403.Peer-Reviewed Original ResearchUnsupervised domain adaptationTarget domain modelBackdoor attacksDomain adaptationTraining dataLabeled source domain dataSusceptible to backdoor attacksAccurate pseudo labelsDomain modelSource domain dataPatient data privacyTarget training dataOff-the-shelfPseudo-labelsData privacySource domainMulti-vendorRandom initializationTraining phaseDomain dataDiagnosis modelTarget modelMulti-diseaseAttacksAuxiliary model
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