Deeply-Supervised Learning for Alzheimer’s Disease Rehabilitation


Deeply-Supervised Learning for Alzheimer’s Disease Rehabilitation – Deep learning is a promising method based on deep neural networks to tackle the challenging problem of Alzheimer’s diagnosis. Deep learning provides a rich data source for clinical diagnosis of diseases, and can improve a patient’s diagnosis ability to improve their quality of life. However, due to the rich data sources the medical data cannot be fully processed to extract useful information. In this paper, a novel methodology for the development and use of deep neural networks (DNNs) are presented for diagnosis analysis using large datasets. In this way, DNNs are trained on large datasets and perform well in the training stage. A new model, DeepDeepModel, is proposed with the aim to achieve better classification accuracy. DeepModel aims to learn a deep neural network (DNN) to classify a set of data entries into multiple classes, while providing more interpretability to understand and explore the data. The proposed model is tested on 20 large datasets, using four different sets of samples with different anatomical structures, in order to achieve state of the art performance. DeepModel outperforms other methods especially in the task of diagnosis classification, by over 80% recognition rate.

We propose a novel technique and computational framework for unsupervised clustering of low-level features from a large unlabeled collection of labeled data (e.g., image data) with minimal training set. In order to obtain the best of both worlds, we propose to exploit low-level features and clusters in a highly discriminative manner, which is the best case scenario for our approach. We first apply the method to image data and demonstrate its usefulness for unsupervised clustering based on the similarity between the clustering result and the labels. We further use it to analyze the effect of labels and then evaluate whether or not a high-level clustering method can have the same performance. Experimental results show that using low-level features, which is desirable for image data, outperforms the supervised clustering approach, as well as other clustering methods proposed in this paper.

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Deeply-Supervised Learning for Alzheimer’s Disease Rehabilitation

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  • Evaluating Neural Networks on ActiveLearning with the Lasso

    Robust Sparse Subspace ClusteringWe propose a novel technique and computational framework for unsupervised clustering of low-level features from a large unlabeled collection of labeled data (e.g., image data) with minimal training set. In order to obtain the best of both worlds, we propose to exploit low-level features and clusters in a highly discriminative manner, which is the best case scenario for our approach. We first apply the method to image data and demonstrate its usefulness for unsupervised clustering based on the similarity between the clustering result and the labels. We further use it to analyze the effect of labels and then evaluate whether or not a high-level clustering method can have the same performance. Experimental results show that using low-level features, which is desirable for image data, outperforms the supervised clustering approach, as well as other clustering methods proposed in this paper.


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