2024
Calibrating Multi-modal Representations: A Pursuit of Group Robustness without Annotations
You C, Min Y, Dai W, Sekhon J, Staib L, Duncan J. Calibrating Multi-modal Representations: A Pursuit of Group Robustness without Annotations. 2015 IEEE Conference On Computer Vision And Pattern Recognition (CVPR) 2024, 00: 26140-26150. PMID: 39640960, PMCID: PMC11620289, DOI: 10.1109/cvpr52733.2024.02470.Peer-Reviewed Original ResearchDiverse downstream tasksVision-language modelsPre-trained modelsRepresentation of samplesContrastive learningDownstream tasksFeature reweightingTraining dataFeature patternsModel generalizationGroup annotationsPain pointsGroup labelsAnnotationRobustnessClassifierClipsFeaturesDeepDeploymentBenchmarksTime-intensiveCodeTaskLearning
2022
Machine Learning in Differentiating Gliomas from Primary CNS Lymphomas: A Systematic Review, Reporting Quality, and Risk of Bias Assessment
Petersen G, Shatalov J, Verma T, Brim WR, Subramanian H, Brackett A, Bahar RC, Merkaj S, Zeevi T, Staib LH, Cui J, Omuro A, Bronen RA, Malhotra A, Aboian MS. Machine Learning in Differentiating Gliomas from Primary CNS Lymphomas: A Systematic Review, Reporting Quality, and Risk of Bias Assessment. American Journal Of Neuroradiology 2022, 43: 526-533. PMID: 35361577, PMCID: PMC8993193, DOI: 10.3174/ajnr.a7473.Peer-Reviewed Original ResearchConceptsMachine learning-based methodsLearning-based methodsBalanced data setData setsVector machine modelMachine learningClassification algorithmsMachine modelMachineAlgorithmData basesPrediction modelPromising resultsPrimary CNS lymphomaPrediction model study RiskRisk of biasRadiomic featuresClassifierSetCNS lymphomaWebLearningFeaturesQualitySystematic review