2024
Non-invasive Electrolyte Estimation Using Multi-lead ECG Data via Semi-Supervised Contrastive Learning with an Adaptive Loss
Nowroozilarki Z, Huang S, Khera R, Mortazavi B. Non-invasive Electrolyte Estimation Using Multi-lead ECG Data via Semi-Supervised Contrastive Learning with an Adaptive Loss. 2024, 00: 1-8. DOI: 10.1109/bhi62660.2024.10913552.Peer-Reviewed Original ResearchState-of-the-art modelsAdaptive lossSemi-supervised contrastive learningTrain machine learning-based modelsState-of-the-artClassification of electrocardiogramElectronic health record datasetLearning-based modelsMachine learning-based modelsContrastive learningLabel scarcityUnlabeled datasetRegression tasksClassification taskECG-dataRecord datasetData pointsLabeling frequencyDatasetTaskDataBackpropagationEncodingAccurate predictionLabeling
2023
Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective.
You C, Dai W, Min Y, Liu F, Clifton D, Zhou S, Staib L, Duncan J. Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective. Advances In Neural Information Processing Systems 2023, 36: 9984-10021. PMID: 38813114, PMCID: PMC11136570.Peer-Reviewed Original ResearchMedical image segmentationContrastive learningImage segmentationSemi-supervised medical image segmentationSemi-supervised contrastive learningSelf-supervised objectiveSemantic segmentation datasetsSemi-supervised methodGround-truth labelsQuality of visual representationSafety-critical tasksSegmentation datasetTail classesSegmentation taskLabel setsTruth labelsCL frameworkNegative examplesModel collapseVariance-reductionVariance-reduction techniquesVisual representationTaskLearningPairs of samples
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