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Recent advances in machine learning yielded new techniques to train deep neural networks, which resulted in highly successful applications in many pattern recognition tasks such as object detection and speech recognition. In this paper we provide a head-to-head comparison between a state-of-the art in mammography CAD system, relying on a manually designed(More)
To get a robot to perform tasks autonomously, the robot has to plan its behavior and make decisions based on the input it receives. Unfortunately, contemporary robot sensors and actuators are subject to noise, rendering optimal decision making a stochastic process. To model this process, partially observable Markov decision processes (POMDPs) can be(More)
Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for(More)
Neural networks, in particular deep Convolutional Neural Networks (CNN), have recently gone through a renaissance sparked by the introduction of more efficient training procedures and massive amounts of raw annotated data. Barring a handful of modalities, medical images are typically too large to present as input as a whole and models are consequently(More)
Augmented Reality is an emerging research field, that aims for the composition of real and virtual imagery, by means of a camera and display device. Spatial augmented reality employs data projectors to augment the real world. In this setting, traditional tracking methods fall short due to the interference caused by the projector. Recent works assume a(More)
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