3D Scanning Network for Segmentation of Medical Images – Deep-learning methods have been successfully applied to the design of medical domain applications and to medical imaging. However, deep models, such as deep neural networks (DNNs), do not exhibit robustness when applied to medical data. In this paper, we propose a hybrid, deep-learning-centric, efficient and scalable deep-learning method to enhance the performance of DNNs and other deep-learning-based approaches. The proposed method aims to improve the performance of DNNs by enhancing some discriminative representations of the data using deep learning. The proposed method is tested in three different medical domain applications, the first in an online MRI data set for the purpose of validation. The performance improvements are achieved with different DNN models, for which DNNs are not available and for which deep models are not implemented. In this paper, we perform a systematic empirical evaluation of our DNN-based deep-learning method for improving the performance of DNN-based deep vision approaches. The results indicate that the proposed method is competitive in terms of its effectiveness and efficiency.
We propose a novel deep-learnable variant of the widely known adversarial learning algorithm, with a different theoretical foundation to the traditional learning algorithms. Our novel architecture is designed to address a fundamental bottleneck in deep-learning – the lack of large, compactly learned features for supervised learning and generalization. We develop a novel and simple neural network model to automatically learn the feature vector to be used in adversarial search on a large-scale distribution, and use the feature vectors to train the neural network for learning. Furthermore, the model is designed to be easy to implement and scalable, which allows us to implement the new adversarial search algorithm with high accuracy on several datasets. We test the proposed algorithm on several publicly available datasets to demonstrate the efficacy of its architecture.
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3D Scanning Network for Segmentation of Medical Images
Sparse Conjugate Gradient Methods for Big Data
SNearest Neighbor Adversarial Search with Binary CodesWe propose a novel deep-learnable variant of the widely known adversarial learning algorithm, with a different theoretical foundation to the traditional learning algorithms. Our novel architecture is designed to address a fundamental bottleneck in deep-learning – the lack of large, compactly learned features for supervised learning and generalization. We develop a novel and simple neural network model to automatically learn the feature vector to be used in adversarial search on a large-scale distribution, and use the feature vectors to train the neural network for learning. Furthermore, the model is designed to be easy to implement and scalable, which allows us to implement the new adversarial search algorithm with high accuracy on several datasets. We test the proposed algorithm on several publicly available datasets to demonstrate the efficacy of its architecture.