A Multi-level Canonical Correlation Analysis Scheme for Standard-dose PET Image Estimation.- Image Super-Resolution by Supervised Adaption of Patchwise Self-Similarity from High-Resolution Image.- Automatic Hippocampus Labeling Using the Hierarchy of Sub-Region Random Forests.- Isointense Infant Brain Segmentation by Stacked Kernel Canonical Correlation Analysis.- Improving Accuracy of Automatic Hippocampus Segmentation in Routine MRI by Features Learned from Ultra-high Field MRI.- Dual-Layer l1-Graph Embedding for Semi-Supervised Image Labeling.- Automatic Liver Tumor Segmentation in Follow-up CT Studies Using Convolutional Neural Network.- Block-based Statistics for Robust Non-Parametric Morphometry.- Automatic Collimation Detection in Digital Radiographs with the Directed Hough Transform and Learning-based Edge Detection.- Efficient Lung Cancer Cell Detection with Deep Convolutional Neural Network.- An Effective Approach for Robust Lung Cancer Cell Detection.- Laplacian Shape Editing with Local Patch Based Force Field for Interactive Segmentation.- Hippocampus Segmentation through Distance Field Fusion.- Learning a Spatiotemporal Dictionary for Magnetic Resonance Fingerprinting with Compress Sensing.- Fast Regions-of-Interest Detection in Whole Slide Histopathology Images.- Reliability Guided Forward and Backward Patch-based Method for Multi-atlas Segmentation.- Correlating Tumour Histology and ex vivo MRI Using Dense Modality-Independent Patch-Based Descriptor.- Multi-Atlas Segmentation using Patch-Based Joint Label Fusion with Non-Negative Least Squares Regression.- A Spatially Constrained Deep Learning Framework for Detection of Epithelial Tumor Nuclei in Cancer Histology Images.- 3D MRI Denoising using Rough Set Theory and Kernel Embedding Method.- A Novel Cell Orientation Congruence Descriptor for Superpixel based Epithelium Segmentation in Endometrial Histology Images.- Patch-based Segmentation from MP2RAGE Images: Comparison to Conventional Techniques.- Multi-Atlas and Multi-Modal Hippocampus Segmentation for Infant MR Brain Images by Propagating Anatomical Labels on Hypergraph.- Prediction of Infant MRI Appearance and Anatomical Structure Evolution using Sparse Patch-based Metamorphosis Learning Framework.- Efficient Multi-Scale Patch-based Segmentation.