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Titlebook: Machine Learning in Medical Imaging; 7th International Wo Li Wang,Ehsan Adeli,Heung-Il Suk Conference proceedings 2016 Springer Internation

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楼主: deduce
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Bilateral Regularization in Reproducing Kernel Hilbert Spaces for Discontinuity Preserving Image Res bilateral regularizer in order to guarantee a finite dimensional solution. In addition, we build direction-dependent basis functions within the SKM framework in order to elongate the transformations along the potential sliding organ boundaries. In the experiments, we evaluate the registration resu
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Learning Appearance and Shape Evolution for Infant Image Registration in the First Year of Life,ppearance change between two new infant subjects. After that, it becomes much easier to deploy any conventional image registration method to complete the remaining registration since the above challenges for current state-of-the-art registration methods have been solved successfully. We apply our pr
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,Deep Ensemble Sparse Regression Network for Alzheimer’s Disease Diagnosis,nal neural network by taking the target-level representations as input. Our deep network learns to optimally fuse the predicted response variables, ., target-level representations, from the same sparse response model(s) and also those from the neighboring sparse response models. To our best knowledg
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