An Analysis Of Violence Victimization, Substance Use, And Gender As Predictors Of Violence Perpetration Among Adolescents

The present study examined potential predictors for interpersonal violence perpetration among adolescents, focusing primarily on reported violence victimization but also analyzing reported substance use and gender. I made correlational assessments using 11,306 responses (mean age, 16.1, SD = 1.2) from the Youth Risk Behavior Surveillance System. I focused on questions related to violence perpetration (physical or sexual), to violence victimization (physical or sexual), and to substance use (alcohol bingeing or marijuana use). I ran a simultaneous binomial logistic regression as well as separate chi-square tests to test the strength of the correlations between each categorical predictor and violence perpetration. Participants who reported violence victimization were at three times higher risk of perpetrating violence than those who reported no victimization. Moreover, adolescents who reported substance use were at twice the risk of perpetrating violence than nonusers; this was the case both for alcohol users and for marijuana users. Finally, the risk of violence perpetration among males was almost three times that of females. Given the correlations observed in the current study, preventative efforts may focus on adolescent victimization, substance use habits, and gendered socialization as a means to reduce the prevalence of violence perpetration among adolescents

Visualizing convolutional neural networks to improve decision support for skin lesion classification

Because of their state-of-the-art performance in computer vision, CNNs are becoming increasingly popular in a variety of fields, including medicine. However, as neural networks are black box function approximators, it is difficult, if not impossible, for a medical expert to reason about their output. This could potentially result in the expert distrusting the network when he or she does not agree with its output. In such a case, explaining why the CNN makes a certain decision becomes valuable information. In this paper, we try to open the black box of the CNN by inspecting and visualizing the learned feature maps, in the field of dermatology. We show that, to some extent, CNNs focus on features similar to those used by dermatologists to make a diagnosis. However, more research is required for fully explaining their output.Comment: 8 pages, 6 figures, Workshop on Interpretability of Machine Intelligence in Medical Image Computing at MICCAI 201

Quantification in cardiac MRI: advances in image acquisition and processing

Cardiac magnetic resonance (CMR) imaging enables accurate and reproducible quantification of measurements of global and regional ventricular function, blood flow, perfusion at rest and stress as well as myocardial injury. Recent advances in MR hardware and software have resulted in significant improvements in image quality and a reduction in imaging time. Methods for automated and robust assessment of the parameters of cardiac function, blood flow and morphology are being developed. This article reviews the recent advances in image acquisition and quantitative image analysis in CMR