An Adaptive Sampling Scheme to Efficiently Train Fully Convolutional Networks for Semantic Segmentation

Deep convolutional neural networks (CNNs) have shown excellent performance in object recognition tasks and dense classification problems such as semantic segmentation. However, training deep neural networks on large and sparse datasets is still challenging and can require large amounts of computation and memory. In this work, we address the task of performing semantic segmentation on large data sets, such as three-dimensional medical images. We propose an adaptive sampling scheme that uses a-posterior error maps, generated throughout training, to focus sampling on difficult regions, resulting in improved learning. Our contribution is threefold: 1) We give a detailed description of the proposed sampling algorithm to speed up and improve learning performance on large images. We propose a deep dual path CNN that captures information at fine and coarse scales, resulting in a network with a large field of view and high resolution outputs. We show that our method is able to attain new state-of-the-art results on the VISCERAL Anatomy benchmark

New Petro‐aggression in the Middle East: Saudi Arabia in the Spotlight

That hydrocarbon abundance may lead to more violence is an established truism in the literature on the resource curse. Looking at the Middle East, however, the literature relates bellicose state behaviour entirely to oil-producing revolutionary republics. Instead, dynastic monarchies are claimed to be the more peacefully behaving actors. Current developments turn this conclusion upside down, however. Since 2015 at the latest, the foreign policy of Saudi Arabia, the leading monarchy in the Middle East, has transformed from multi-dependence to petro-aggression. By discussing this striking transformation, the paper puts forward a framework looking at the interaction of three crucial dimensions: first, the decreasing power projection towards the Middle East by the United States, the decade-long hegemon, due to gradual changes in world energy markets and war fatigue at home; second, the lasting fiscal potency of the Saudi regime; and, third, the personalization of the Saudi monarchy under King Salman as a historically contingent result of transferring power to the generation of Ibn Saud's grandsons

Overview of the 2014 Workshop on Medical Computer Vision—Algorithms for Big Data (MCV 2014)

The 2014 workshop on medical computer vision (MCV): algorithms for big data took place in Cambridge, MA, USA in connection with MICCAI (Medical Image Computing for Computer Assisted Intervention). It is the fourth MICCAI MCV workshop after those held in 2010, 2012 and 2013 with another edition held at CVPR 2012. This workshop aims at exploring the use of modern computer vision technology in tasks such as automatic segmentation and registration, localisation of anatomical features and extraction of meaningful visual features. It emphasises questions of harvesting, organising and learning from large-scale medical imaging data sets and general-purpose automatic understanding of medical images. The workshop is especially interested in modern, scalable and efficient algorithms which generalise well to previously unseen images.The strong participation in the workshop of over 80 persons shows the importance of and interest in Medical Computer Vision. This overview article describes the papers presented in the workshop as either oral presentations or short presentations and posters. It also describes the invited talks and the results of the VISCERAL session in the workshop on the use of big data in medical imaging