Convolutional Convolutional Neural Networks for Brain Lesions Detection – This paper presents a method for multi-label clustering of brain images by training a Convolutional Neural Network (CNN). By combining a multi-label model with a CNN, we can train CNNs to predict how different parts of the scene are represented by multiple labels. This can be implemented as a preprocessing step for CNNs, and further incorporated into the regularization term of the CNN architecture. The trained CNNs are then used to learn representations of different types of labels, which can learn representations over multiple levels of labels. We show empirically that the learning rate of CNNs can be significantly improved by using CNNs trained for different levels of labels. On average, the performance of the CNNs is reduced by 0.03% to 0.33% using the CNN training setup.

The problem of predicting the visual object appearance, given a given object, has been recently explored in various forms of learning, including image processing and object detection. In this work, we explore the task of predicting the object appearance, given its visual appearance (e.g., color, texture), as a function of the object’s orientation, illumination, pose, and pose-based properties. We propose a novel and simple method for learning from data obtained from 3D point clouds. Using a novel deep Convolutional Neural Network model constructed from the pixel-wise local contrast and spatial contrast information, we show that predicting the object appearance for 3D point clouds can be used to accurately predict the pose of the object. Through simulations, we find significant improvements for our prediction for 3D point clouds over existing state-of-the-art models.

Learning from the Fallen: Deep Cross Domain Embedding

Provenance-relaxed feature selection for semi-supervised deep networks

Convolutional Convolutional Neural Networks for Brain Lesions Detection

A Medical Image Segmentation Model Ensembles From 100+ Classifiers

Conceptual Constraint-based Neural NetworksThe problem of predicting the visual object appearance, given a given object, has been recently explored in various forms of learning, including image processing and object detection. In this work, we explore the task of predicting the object appearance, given its visual appearance (e.g., color, texture), as a function of the object’s orientation, illumination, pose, and pose-based properties. We propose a novel and simple method for learning from data obtained from 3D point clouds. Using a novel deep Convolutional Neural Network model constructed from the pixel-wise local contrast and spatial contrast information, we show that predicting the object appearance for 3D point clouds can be used to accurately predict the pose of the object. Through simulations, we find significant improvements for our prediction for 3D point clouds over existing state-of-the-art models.