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Prediction of Cognitive Decline in Parkinson's Disease using Hybrid Machine Learning and Deep Learning Techniques.
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Deep versus handcrafted tensor radiomics features: application to survival prediction in head and neck cancer
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Robustness and Reproducibility of Radiomics Features from Fusions of PET-CT Images
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Longitudinal Clustering Analysis and Prediction of Parkinson’s Disease Progression
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Reliable and Reproducible Tensor Radiomics Features in Prediction of Survival in Head and Neck Cancer
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Hybrid Machine Learning Methods and Ensemble Voting for Identification of Parkinson’s Disease Subtypes
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Robust identification of Parkinson’s disease subtypes using radiomics and hybrid machine learning
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Cognitive Outcome Prediction in Parkinson’s Disease Using Hybrid Machine Learning Systems and Radiomics Features
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Multi-Modality Fusion Coupled with Deep Learning for Improved Outcome Prediction in Head and Neck Cancer
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Drug Amount Prediction in Parkinson’s Disease Using Hybrid Machine Learning Systems and Radiomics Features
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Prediction of TNM Stage in Head and Neck Cancer Using Hybrid Machine Learning Systems and Radiomics Features
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Advanced Survival Prediction in Head and Neck Cancer Using Hybrid Machine Learning Systems and Radiomics Features
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Feasibility study of synthesis from CT to PET using CycleGAN network for head and neck cancer patients
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Breast cancer diagnosis using an integrated radiomic framework and machine learning methods based on DCE-MRI images
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Developing SERA Software and Adding Image Filtering
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