2024
CLEFT: Language-Image Contrastive Learning with Efficient Large Language Model and Prompt Fine-Tuning
Du Y, Chang B, Dvornek N. CLEFT: Language-Image Contrastive Learning with Efficient Large Language Model and Prompt Fine-Tuning. Lecture Notes In Computer Science 2024, 15012: 465-475. DOI: 10.1007/978-3-031-72390-2_44.Peer-Reviewed Original ResearchContrastive Language-Image Pre-trainingLanguage modelState-of-the-art performanceSelf-supervised representation learningContrastive learning methodFine-tuningProlonged training timeBERT encoderContrastive learningRepresentation learningClass labelsGPU resourcesTraining samplesTraining timeMammography datasetModel sizePre-trainingLearning methodsEfficient frameworkVisual modelRichness of informationDatasetClinical diagnostic dataLearningMedical applicationsSIFT-DBT: Self-Supervised Initialization and Fine-Tuning for Imbalanced Digital Breast Tomosynthesis Image Classification
Du Y, Hooley R, Lewin J, Dvornek N. SIFT-DBT: Self-Supervised Initialization and Fine-Tuning for Imbalanced Digital Breast Tomosynthesis Image Classification. 2011 IEEE International Symposium On Biomedical Imaging: From Nano To Macro 2024, 00: 1-5. PMID: 39263046, PMCID: PMC11386909, DOI: 10.1109/isbi56570.2024.10635723.Peer-Reviewed Original Research
2023
Chapter 13 Deep learning with connectomes
Dvornek N, Li X. Chapter 13 Deep learning with connectomes. 2023, 289-308. DOI: 10.1016/b978-0-323-85280-7.00013-0.ChaptersDeep learning modelsLearning modelDeep learningClassic computer visionNeural network architectureImage analysis problemsMachine learning methodsNeural network modelComputer visionPotential future workNetwork architectureNonlinear neural network modelArt resultsPrediction taskLearning methodsNetwork modelAnalysis problemUseful representationConnectomePopular typeLearningFuture workData analysisArchitectureTask