Artificial intelligence (AI)-based methods have emerged as powerful tools to transform medical care. Although machine learning classifiers (MLCs) have already demonstrated strong performance in image-based diagnoses, analysis of diverse and massive electronic health record (EHR) data remains challenging. Here, we show that MLCs can query EHRs in a manner similar to the hypothetico-deductive reasoning used by physicians and unearth associations that previous statistical methods have not found. Our model applies an automated natural language processing system using deep learning techniques to extract clinically relevant information from EHRs. In total, 101.6 million data points from 1,362,559 pediatric patient visits presenting to a major referral center were analyzed to train and validate the framework.

In this tutorial you will learn how to train a simple Convolutional Neural Network (CNN) with Keras on the Fashion MNIST dataset, enabling you to classify fashion images and categories. The Fashion MNIST dataset is meant to be a (slightly more challenging) drop-in replacement for the (less challenging) MNIST dataset. Throughout this tutorial, you will learn how to train a simple Convolutional Neural Network (CNN) with Keras on the Fashion MNIST dataset, giving you not only hands-on experience working with the Keras library but also your first taste of clothing/fashion classification. To learn how to train a Keras CNN on the Fashion MNIST dataset, just keep reading! In the first part of this tutorial, we will review the Fashion MNIST dataset, including how to download it to your system.

Sparse reward is one of the most challenging problems in reinforcement learning (RL). Hindsight Experience Replay (HER) attempts to address this issue by converting a failed experience to a successful one by relabeling the goals. Despite its effectiveness, HER has limited applicability because it lacks a compact and universal goal representation. We present Augmenting experienCe via TeacheR's adviCE (ACTRCE), an efficient reinforcement learning technique that extends the HER framework using natural language as the goal representation. We first analyze the differences among goal representation, and show that ACTRCE can efficiently solve difficult reinforcement learning problems in challenging 3D navigation tasks, whereas HER with non-language goal representation failed to learn. We also show that with language goal representations, the agent can generalize to unseen instructions, and even generalize to instructions with unseen lexicons. We further demonstrate it is crucial to use hindsight advice to solve challenging tasks, and even small amount of advice is sufficient for the agent to achieve good performance.

Within OSNs, many of our supposedly online friends may instead be fake accounts called social bots, part of large groups that purposely re-share targeted content. Here, we study retweeting behaviors on Twitter, with the ultimate goal of detecting retweeting social bots. We collect a dataset of 10M retweets. We design a novel visualization that we leverage to highlight benign and malicious patterns of retweeting activity. In this way, we uncover a 'normal' retweeting pattern that is peculiar of human-operated accounts, and 3 suspicious patterns related to bot activities. Then, we propose a bot detection technique that stems from the previous exploration of retweeting behaviors. Our technique, called Retweet-Buster (RTbust), leverages unsupervised feature extraction and clustering. An LSTM autoencoder converts the retweet time series into compact and informative latent feature vectors, which are then clustered with a hierarchical density-based algorithm. Accounts belonging to large clusters characterized by malicious retweeting patterns are labeled as bots. RTbust obtains excellent detection results, with F1 = 0.87, whereas competitors achieve F1 < 0.76. Finally, we apply RTbust to a large dataset of retweets, uncovering 2 previously unknown active botnets with hundreds of accounts.

Manual counts of mitotic figures, which are determined in the tumor region with the highest mitotic activity, are a key parameter of most tumor grading schemes. It is however strongly dependent on the area selection. To reduce potential variability of prognosis due to this, we propose to use an algorithmic field of interest prediction to assess the area of highest mitotic activity in a whole-slide image. Methods: We evaluated two state-of-the-art methods, all based on the use of deep convolutional neural networks on their ability to predict the mitotic count in digital histopathology slides. We evaluated them on a novel dataset of 32 completely annotated whole slide images from canine cutaneous mast cell tumors (CMCT) and one publicly available human mamma carcinoma (HMC) dataset. We first compared the mitotic counts (MC) predicted by the two models with the ground truth MC on both data sets. Second, for the CMCT data set, we compared the computationally predicted position and MC of the area of highest mitotic activity with size-equivalent areas selected by eight veterinary pathologists. Results: We found a high correlation between the mitotic count as predicted by the models (Pearson's correlation coefficient between 0.931 and 0.962 for the CMCT data set and between 0.801 and 0.986 for the HMC data set) on the slides. For the CMCT data set, this is also reflected in the predicted position representing mitotic counts in mostly the upper quartile of the slide's ground truth MC distribution. Further, we found strong differences between experts in position selection. Conclusion: While the mitotic counts in areas selected by the experts substantially varied, both algorithmic approaches were consistently able to generate a good estimate of the area of highest mitotic count. To achieve better inter-rater agreement, we propose to use computer-based area selection for manual mitotic count.

In this work, we have proposed several enhancements to improve the performance of any facial emotion recognition (FER) system. We believe that the changes in the positions of the fiducial points and the intensities capture the crucial information regarding the emotion of a face image. We propose the use of the gradient and the Laplacian of the input image together with the original input into a convolutional neural network (CNN). These modifications help the network learn additional information from the gradient and Laplacian of the images. However, the plain CNN is not able to extract this information from the raw images. We have performed a number of experiments on two well known datasets KDEF and FERplus. Our approach enhances the already high performance of state-of-the-art FER systems by 3 to 5%.

Abstract--Cardiovascular disease (CVD) is the global leading cause of death. A strong risk factor for CVD events is the amount of coronary artery calcium (CAC). To meet demands of the increasing interest in quantification of CAC, i.e. coronary calcium scoring,especially as an unrequested finding for screening and research, automatic methods have been proposed. Current automatic calcium scoring methods are relatively computationally expensive and only provide scores for one type of CT. To address this, we propose a computationally efficient method that employs two ConvNets: the first performs registration to align the fields of view of input CTs and the second performs direct regression of the calcium score, thereby circumventing timeconsuming intermediateCAC segmentation. Optional decision feedback provides insight in the regions that contributed to the calcium score. Experiments were performed using 903 cardiac CT and 1,687 chest CT scans. The method predicted calcium scores in less than 0.3 s. Intra-class correlation coefficient between predicted and manual calcium scores was 0.98 for both cardiac and chest CT. The method showed almost perfect agreement between automatic and manual CVD risk categorization in both datasets, with a linearly weighted Cohen's kappa of 0.95 in cardiac CT and 0.93 in chest CT. Performance is similar to that of state-of-the-art methods, but the proposed method is hundreds of times faster. By providing visual feedback, insight is given in the decision process, making it readily implementable in clinical and research settings. I. INTRODUCTION Cardiovascular disease (CVD) is the global leading cause of death [1]. To reduce the burden of cardiovascular disease the World Health Organization underlines the need for early detection and treatment of individuals with CVD or those who are at high cardiovascular risk due to the presence of one or more risk factors [2]. Quantification of CAC, i.e. calcium scoring, is typically performed in dedicated Personal use of this material is permitted. However, permission to use this material for any other purposes must be obtained from the IEEE by sending a request to pubs-permissions@ieee.org. Bob D. de Vos, Jelmer M. Wolterink, Nikolas Lessmann, and Ivana Išgum are with the Image Sciences Institute of the University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands.

In general the separation problem is inherently ill-posed; however, with enough structural assumption on X and N, it has been established that separation is possible. Depending on the application one might be interested in estimating only X (in this case, N is considered as the corruption), which is referred to as denoising, or in recovering both X and N which is referred to as demixing. Both demixing and denoising arise in a variety of important practical applications in the areas of signal/image processing, computer vision, machine learning, and statistics [Chen et al., 2001, Elad et al., 2005, Bobin et al., 2007, Candès et al., 2011]. Most of the existing techniques assume some prior knowledge on the structures of X and N in order to recover the desired component signal(s). Prior knowledge about the structure of X and N can only be obtained if one has access to the generative mechanism of the signals or has access to clean samples from the probability distribution defined over sets X and N . In many practical settings, neither of these may be feasible. In this paper, we consider the problem of separating constituent signals from superposed observations when clean access to samples from the distribution is not available.

State-of-the-art models are now trained with billions of parameters, reaching hardware limits in terms of memory consumption. This has created a recent demand for memory-efficient optimizers. To this end, we investigate the limits and performance tradeoffs of memory-efficient adaptively preconditioned gradient methods. We propose extreme tensoring for high-dimensional stochastic optimization, showing that an optimizer needs very little memory to benefit from adaptive preconditioning. Our technique applies to arbitrary models (not necessarily with tensor-shaped parameters), and is accompanied by regret and convergence guarantees, which shed light on the tradeoffs between preconditioner quality and expressivity. On a large-scale NLP model, we reduce the optimizer memory overhead by three orders of magnitude, without degrading performance.

Variational autoencoders are powerful algorithms for identifying dominant latent structure in a single dataset. In many applications, however, we are interested in modeling latent structure and variation that are enriched in a target dataset compared to some background---e.g. enriched in patients compared to the general population. Contrastive learning is a principled framework to capture such enriched variation between the target and background, but state-of-the-art contrastive methods are limited to linear models. In this paper, we introduce the contrastive variational autoencoder (cVAE), which combines the benefits of contrastive learning with the power of deep generative models. The cVAE is designed to identify and enhance salient latent features. The cVAE is trained on two related but unpaired datasets, one of which has minimal contribution from the salient latent features. The cVAE explicitly models latent features that are shared between the datasets, as well as those that are enriched in one dataset relative to the other, which allows the algorithm to isolate and enhance the salient latent features. The algorithm is straightforward to implement, has a similar run-time to the standard VAE, and is robust to noise and dataset purity. We conduct experiments across diverse types of data, including gene expression and facial images, showing that the cVAE effectively uncovers latent structure that is salient in a particular analysis.