Structural–temporal embedding of large-scale dynamic networks with parallel implementation

Due to the widespread network data in the real world, network analysis has attracted increasing attention in recent years. In complex systems such as social networks, entities and their mutual relations can be respectively represented by nodes and edges composing a network. Because occurrences of entities and relations in these systems are often dynamic over time, their networks are called temporal networks describing the process of dynamic connection of nodes in the networks. Dynamic network embedding aims to embed nodes in a temporal network into a low-dimensional semantic space, such that the network structures and evolution patterns can be preserved as much as possible in the latent space. Most existing methods capture structural similarities (relations) of strongly-connected nodes based on their historical neighborhood information, they ignore the structural similarities of weakly-connected nodes that may also represent relations and include no explicit temporal information in node embeddings for capturing periodic dependency of events. To address these issues, we propose a novel temporal network embedding model by extending the structure similarity to cover both strong connections and weak connections among nodes, and including the temporal information in node embeddings. To improve the training efficiency of our model, we present a parallel training strategy to quickly acquire node embeddings. Extensive experiments on several real-world temporal networks demonstrate that our model significantly outperforms the state-of-the-arts in traditional tasks, including link prediction and node classification

Plane identification in fetal ultrasound images using saliency maps and convolutional neural networks

Fetal development is noninvasively assessed by measuring the size of different structures in ultrasound (US) images. The reliability of these measurements is dependent upon the identification of the correct anatomical viewing plane, each of which contains different fetal structures. However, the automatic classification of the anatomical planes in fetal US images is challenging due to a number of factors, such as low signal-to-noise-ratios and the small size of the fetus. Current approaches for plane classification are limited to simpler subsets of the problem: only classifying planes within specific body regions or using temporal information from videos. In this paper, we propose a new general method for the classification of anatomical planes in fetal US images. Our method trains two convolutional neural networks to learn the best US and saliency features. The fusion of these features overcomes the challenges associated with US fetal imaging by emphasising the salient features within US images that best discriminate different planes. Our method achieved higher classification accuracy than a state-of-the-art baseline for 12 of the 13 different planes found in a clinical dataset of fetal US images. © 2016 IEEE

Automatic measurement of thalamic diameter in 2-D fetal ultrasound brain images using shape prior constrained regularized level sets

© 2013 IEEE. We derived an automated algorithm for accurately measuring the thalamic diameter from 2-D fetal ultrasound (US) brain images. The algorithm overcomes the inherent limitations of the US image modality: Nonuniform density; missing boundaries; and strong speckle noise. We introduced a 'guitar' structure that represents the negative space surrounding the thalamic regions. The guitar acts as a landmark for deriving the widest points of the thalamus even when its boundaries are not identifiable. We augmented a generalized level-set framework with a shape prior and constraints derived from statistical shape models of the guitars; this framework was used to segment US images and measure the thalamic diameter. Our segmentation method achieved a higher mean Dice similarity coefficient, Hausdorff distance, specificity, and reduced contour leakage when compared to other well-established methods. The automatic thalamic diameter measurement had an interobserver variability of-0.56 2.29 mm compared to manual measurement by an expert sonographer. Our method was capable of automatically estimating the thalamic diameter, with the measurement accuracy on par with clinical assessment. Our method can be used as part of computer-assisted screening tools that automatically measure the biometrics of the fetal thalamus; these biometrics are linked to neurodevelopmental outcomes