Search alternatives:
feature optimization » resource optimization (Expand Search), feature elimination (Expand Search), structure optimization (Expand Search)
model optimization » codon optimization (Expand Search), global optimization (Expand Search), based optimization (Expand Search)
image feature » image features (Expand Search), scale feature (Expand Search), imaging features (Expand Search)
wave model » naive model (Expand Search), game model (Expand Search), base model (Expand Search)
feature optimization » resource optimization (Expand Search), feature elimination (Expand Search), structure optimization (Expand Search)
model optimization » codon optimization (Expand Search), global optimization (Expand Search), based optimization (Expand Search)
image feature » image features (Expand Search), scale feature (Expand Search), imaging features (Expand Search)
wave model » naive model (Expand Search), game model (Expand Search), base model (Expand Search)
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Data_Sheet_1_Alzheimer’s Disease Diagnosis and Biomarker Analysis Using Resting-State Functional MRI Functional Brain Network With Multi-Measures Features and Hippocampal Subfield...
Published 2022“…Finally, we implemented and compared the different feature selection algorithms to integrate the structural features, brain networks, and voxel features to optimize the diagnostic identifications of AD using support vector machine (SVM) classifiers. …”
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DataSheet_1_Exploring deep learning radiomics for classifying osteoporotic vertebral fractures in X-ray images.docx
Published 2024“…Logistic regression emerged as the optimal machine learning algorithm for both DLR models. …”
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23
DataSheet_1_Multi-Parametric MRI-Based Radiomics Models for Predicting Molecular Subtype and Androgen Receptor Expression in Breast Cancer.docx
Published 2021“…A total of 4,198 radiomics features were extracted from the pre-biopsy multi-parametric MRI (including dynamic contrast-enhancement T1-weighted images, fat-suppressed T2-weighted images, and apparent diffusion coefficient map) of each patient. …”