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
2019
Prediction of distant melanoma recurrence from primary tumor digital H&E images using deep learning.
Robinson E, Kulkarni P, Pradhan J, Gartrell R, Yang C, Rizk E, Acs B, Rohr B, Phelps R, Ferringer T, Horst B, Rimm D, Wang J, Saenger Y. Prediction of distant melanoma recurrence from primary tumor digital H&E images using deep learning. Journal Of Clinical Oncology 2019, 37: 9577-9577. DOI: 10.1200/jco.2019.37.15_suppl.9577.Peer-Reviewed Original ResearchDeep neural net architectureOpen source softwareRecurrent neural networkNeural net architectureDigital pathology toolsDeep learningSource softwareNet architectureFeature informationNeural networkNetwork parametersTIFF filesAdjuvant immunotherapyMelanoma recurrenceCohort 2Cohort 1Cell classificationStage IMultivariable Cox proportional hazards modelsDNNCox proportional hazards modelColumbia University Medical CenterNuclear segmentationEvidence of diseaseIndependent prognostic factorNetPyNE, a tool for data-driven multiscale modeling of brain circuits
Dura-Bernal S, Suter BA, Gleeson P, Cantarelli M, Quintana A, Rodriguez F, Kedziora DJ, Chadderdon GL, Kerr CC, Neymotin SA, McDougal RA, Hines M, Shepherd GM, Lytton WW. NetPyNE, a tool for data-driven multiscale modeling of brain circuits. ELife 2019, 8: e44494. PMID: 31025934, PMCID: PMC6534378, DOI: 10.7554/elife.44494.Peer-Reviewed Original ResearchConceptsInformation-theoretic measuresDeclarative languageImplementation codeGraphical interfaceNetPyNENetwork parametersTheoretic measuresNetwork modelNeuron networkStandardized formatUsersMillions of cellsConnectivity rulesExperimental datasetsNetworkRaster plotMultiscale network modelMultiple scalesDatasetToolModelersSpecificationModel parametersModelingVisualization
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