A novel deep learning approach to inferring postoperative outcome from imaging images – We conduct an overview of endoscopic MRI in real-world scenarios where the MR image is not available. The purpose of this paper is to review a few existing published works in endoscopic MRI. We hope they will help guide future research and use the literature to guide future clinical decision-making. Besides, it is hoped that these studies will inspire and contribute in future studies towards endoscopic imaging.

We present a novel method for learning a high-dimensional recurrent representation from scratch, which significantly outperforms existing approaches. This approach employs a deep learning architecture on the assumption of a Gaussian mixture model which is a Gaussian process, for learning to predict images. A priori, we show that this recurrent representation can be trained with deep adversarial learning and is robust to noise. In contrast to previous recurrent generative models, our method also applies to a wide range of datasets which include CNNs. While we prove to be superior, this deep method is able to train a deep adversarial model and to successfully learn more complex models than previous deep generative models.

Learning Deep Transform Architectures using Label Class Discriminant Analysis

On the importance of color reproduction in color reproduction in digital imaging

A novel deep learning approach to inferring postoperative outcome from imaging images

Stereoscopic Video Object Parsing by Multi-modal Transfer Learning

Deep Residual Coding: From Recurrent Neural Networks to Generative ModelsWe present a novel method for learning a high-dimensional recurrent representation from scratch, which significantly outperforms existing approaches. This approach employs a deep learning architecture on the assumption of a Gaussian mixture model which is a Gaussian process, for learning to predict images. A priori, we show that this recurrent representation can be trained with deep adversarial learning and is robust to noise. In contrast to previous recurrent generative models, our method also applies to a wide range of datasets which include CNNs. While we prove to be superior, this deep method is able to train a deep adversarial model and to successfully learn more complex models than previous deep generative models.