paper summary
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CNNs for Brain Tumor Segmentation in MRI Scans (BraTS)
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U-Net: Convolutional Networks for Biomedical Image Segmentation
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Architectures, Datasets, and Transfer Learning for CNN-based CAD
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CNNs for Brain Tumor Segmentation in MRI Scans (BraTS)
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Reinforcement Learning for Landmark Detection in 3D CT Volumes
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Unsupervised Learning for Deformable Registration
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Achieving Dermatologist-level Classification Performance of Skin Lesion Images
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Feature Representation and Multi-modal Fusion using Deep Boltzmann Machine
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V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
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Matching with Shape Contexts
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Robust Point Set Registration Using Gaussian Mixture Models
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Shape Registration Using Information Theory and Free Form Deformations
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Medical Image Registration Using Mutual Information
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Non-rigid Image Registration Using Graph-cuts
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Nonrigid Registration Using Free-Form Deformations
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A Minimum Description Length Approach to Statistical Shape Modeling
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Active Shape Models
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Multiscale Vessel Enhancement Filtering
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Graph Cuts for Image Segmentation
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Interactive Live-Wire Boundary Extraction
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Random Walks for Image Segmentation
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Active Contours Without Edges
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Snakes: Active Contour Models
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Stochastic Active Contour Scheme (STACS) for Image Segmentation
