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

Ambulatory

Dual-Layer Groupwise Registration for Consistent Labeling of Longitudinal Brain Images,mical labeling methods that can respect temporal consistency between images. However, the characteristics of such longitudinal images and how they lodge into the image manifold are often neglected in existing labeling methods. Indeed, most of them independently align atlases to each target time-poin

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,Joint Discriminative and Representative Feature Selection for Alzheimer’s Disease Diagnosis,d to AD progression, many feature selection methods have been proposed to identify informative features (.brain regions) to build an accurate prediction model. These methods mostly only focus on the feature-target relationship to select features which are discriminative to the targets (.diagnosis la

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Conclave

Estimable

,Deep Ensemble Sparse Regression Network for Alzheimer’s Disease Diagnosis,small number of samples. In this paper, we propose a novel framework that utilizes sparse regression models as . learner and builds a deep convolutional neural network for clinical decision making. Specifically, we first train multiple sparse regression models, each of which has different values of

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Learning Representation for Histopathological Image with Quaternion Grassmann Average Network,w unsupervised feature learning algorithm for images via a simple deep network architecture. However, PCA is sensitive to noise and outliers, which may depress the representation learning of PCANet. Grassmann averages (GA) is a newly proposed dimensionality reduction algorithm, which is more robust

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ectropion

Neuralgia

Multi-label Deep Regression and Unordered Pooling for Holistic Interstitial Lung Disease Pattern Deolutions rely on manually provided regions of interest, limiting their clinical usefulness. In addition, no work has yet focused on predicting more than one ILD from the same CT slice, despite the frequency of such occurrences. To address these limitations, we propose two variations of multi-label d