Titlebook: Machine Learning in Medical Imaging; 8th International Wo Qian Wang,Yinghuan Shi,Kenji Suzuki Conference proceedings 2017 Springer Internat

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Dakai Jin,Ziyue Xu,Adam P. Harrison,Kevin George,Daniel J. Molluraluding case studies, best practices and methodologies in env.This book demonstrates the application of Life-cycle Cost Approach (LCCA) in the management of infrastructure and other investment projects in the context of developing countries. The main goal is to identify potential opportunities for th

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Jun Wang,Qian Wang,Shitong Wang,Dinggang Shen life.Asks whether personal immortality is possible.What are life and death? Is it possible to understand their essence and give clear definitions? Countless books and articles have been devoted to trying to answer these intriguing questions. However, there are still no definite and generally accept

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Detection and Localization of Drosophila Egg Chambers in Microscopy Images,is methods require manual segmentation of individual egg chambers, which is a difficult and time-consuming task. We present an image processing pipeline to detect and localize Drosophila egg chambers that consists of the following steps: (i) superpixel-based image segmentation into relevant tissue c

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Growing a Random Forest with Fuzzy Spatial Features for Fully Automatic Artery-Specific Coronary Cafication is to perform non-contrasted coronary computed-tomography (CCT) on a patient and present the resulting image to an expert, who then uses this to label CAC in a tedious and time-consuming process. To improve this situation, we present an automatic CAC labeling system with high clinical pract

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Atlas of Classifiers for Brain MRI Segmentation,asets taking into account both the imaging data and the corresponding labels. It is therefore more informative than the classical probabilistic atlas and more economical than the popular multi-atlas approaches, which require large memory consumption and high computational complexity for each segment

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Dictionary Learning and Sparse Coding-Based Denoising for High-Resolution Task Functional Connectiv the brain functional connectivity via dictionary learning and sparse coding (DLSC). In order to address the limitations of the unsupervised DLSC-based fMRI studies, we utilize the prior knowledge of task paradigm in the learning step to train a data-driven dictionary and to model the sparse represe

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