Automated segmentation of the human brain from magnetic resonance images using a genetic algorithm


Automated segmentation of the human brain from magnetic resonance images using a genetic algorithm – Recently presented methods for the purpose of extracting biologically relevant features from image data are presented. To learn the feature representations of images to improve the extraction performance, a key ingredient is to employ an image-specific feature representation representation as the reference feature vector. This representation is a very challenging task, because it is not easy to use. Most existing approaches generalize to only one image and ignore multiple image data. In this work we explore the use of multiple image feature representations for image extraction using an information-theoretic framework. Specifically, we propose a novel deep learning approach based on the information theoretic framework, which can automatically adapt a feature representation to a new input with the knowledge of its global local minima. We show that our approach can be generalized to any input image. Using the information theoretic framework, we can then evaluate the performance of our method on the task of extracting feature representations, showing that the visual system with more than one image with different features is significantly better than that with fewer images.

Deep learning refers to a class of high-level learning procedures which are trained on the input data, which are then used to learn the classification task. In this paper, we propose the Deep-Learn, Deep-Learning and Deep Learning in Multiple-Task Learning (DLTM) with Deep Learning (DLTM) to learn the task-specific features from the input data by means of deep-learning algorithms. The DLTM-DLTM network can learn feature representations from the data, which are useful for classification in multiple-task learning. In this work, we first analyze its characteristics and propose a novel deep-learning technique for DLTM-DLTM. Next, we propose a novel neural network to learn the features from the input data using Deep Learning. Extensive experiments on five public datasets show that DLTM-DLTM outperforms the state-of-the-art in terms of classification accuracy and network efficiency.

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Automated segmentation of the human brain from magnetic resonance images using a genetic algorithm

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  • Dense2Ad: Densely-Supervised Human-Object Interaction with Deep Convolutional Neural Networks

    A Unified Classification Framework for Information RetrievalDeep learning refers to a class of high-level learning procedures which are trained on the input data, which are then used to learn the classification task. In this paper, we propose the Deep-Learn, Deep-Learning and Deep Learning in Multiple-Task Learning (DLTM) with Deep Learning (DLTM) to learn the task-specific features from the input data by means of deep-learning algorithms. The DLTM-DLTM network can learn feature representations from the data, which are useful for classification in multiple-task learning. In this work, we first analyze its characteristics and propose a novel deep-learning technique for DLTM-DLTM. Next, we propose a novel neural network to learn the features from the input data using Deep Learning. Extensive experiments on five public datasets show that DLTM-DLTM outperforms the state-of-the-art in terms of classification accuracy and network efficiency.


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