2023
Self-Supervised Domain Adaptive Segmentation of Breast Cancer via Test-Time Fine-Tuning
Lee K, Lee H, El Fakhri G, Woo J, Hwang J. Self-Supervised Domain Adaptive Segmentation of Breast Cancer via Test-Time Fine-Tuning. Lecture Notes In Computer Science 2023, 14220: 539-550. DOI: 10.1007/978-3-031-43907-0_52.Peer-Reviewed Original ResearchUnsupervised domain adaptationTarget domainState-of-the-art performanceUnsupervised domain adaptation modelWell-trained deep learning modelDomain adaptation tasksDomain adaptive segmentationState-of-the-artAdaptive feature extractionFine-tuning phaseFeatures of datasetsLarge-scale datasetsDeep learning modelsDomain adaptationUnlabeled dataLabeled dataSegmentation taskNetwork architectureSource domainFeature extractionLatent featuresModel deploymentNetwork parametersBreast cancer datasetAdaptive segmentation
2022
Unsupervised Black-Box Model Domain Adaptation for Brain Tumor Segmentation
Liu X, Yoo C, Xing F, Kuo C, Fakhri G, Kang J, Woo J. Unsupervised Black-Box Model Domain Adaptation for Brain Tumor Segmentation. Frontiers In Neuroscience 2022, 16: 837646. PMID: 35720708, PMCID: PMC9201342, DOI: 10.3389/fnins.2022.837646.Peer-Reviewed Original ResearchUnsupervised domain adaptationDomain adaptationSource domainTarget domainLabeled source domain to unlabeled target domainTransfer of domain knowledgeTarget-specific representationsUnlabeled target domainTarget domain dataKnowledge distillation schemeDeep learning backbonesEntropy minimizationTrained model parametersDifficulty of labelingDomain knowledgeSensitive informationPrivacy concernsPerformance gainsNetwork parametersSegmentation modelDomain dataSource dataCross-center collaborationDistillation schemePotential leaks