2024
Mine yOur owN Anatomy: Revisiting Medical Image Segmentation With Extremely Limited Labels
You C, Dai W, Liu F, Min Y, Dvornek N, Li X, Clifton D, Staib L, Duncan J. Mine yOur owN Anatomy: Revisiting Medical Image Segmentation With Extremely Limited Labels. IEEE Transactions On Pattern Analysis And Machine Intelligence 2024, 46: 11136-11151. PMID: 39269798, DOI: 10.1109/tpami.2024.3461321.Peer-Reviewed Original ResearchMedical image segmentationImage segmentationMedical image segmentation frameworkContext of medical image segmentationLong-tailed class distributionStrong data augmentationsIntra-class variationsSemi-supervised settingData imbalance issueImage segmentation frameworkMedical image analysisMedical image dataSupervision signalsContrastive learningBenchmark datasetsUnsupervised mannerLabel setsData augmentationSegmentation frameworkDomain expertisePseudo-codeImbalance issueModel trainingMedical imagesSegmentation model
2020
Efficient Shapley Explanation for Features Importance Estimation Under Uncertainty
Li X, Zhou Y, Dvornek NC, Gu Y, Ventola P, Duncan JS. Efficient Shapley Explanation for Features Importance Estimation Under Uncertainty. Lecture Notes In Computer Science 2020, 12261: 792-801. PMID: 34308439, PMCID: PMC8299327, DOI: 10.1007/978-3-030-59710-8_77.Peer-Reviewed Original ResearchShapley value explanationMedical image dataDeep learning modelsFeature importance estimationImage dataLearning modelComplex deep learning modelsImportance estimationFeature importance resultsShapley value frameworkInput feature importancePublic neuroimaging datasetComputational complexityShapley explanationsFeature importanceUncertainty estimation methodExplanation workParticular predictionNeuroimaging datasetsMedical fieldImportance resultsImpressive powerEstimation methodUnderlying propertiesImportant approach