DRINet for medical image segmentation

Convolutional neural networks (CNNs) have revolutionized medical image analysis over the past few years. The UNet architecture is one of the most well-known CNN architectures for semantic segmentation and has achieved remarkable successes in many different medical image segmentation applications. The U-Net architecture consists of standard convolution layers, pooling layers, and upsampling layers. These convolution layers learn representative features of input images and construct segmentations based on the features. However, the features learned by standard convolution layers are not distinctive when the differences among different categories are subtle in terms of intensity, location, shape, and size. In this paper, we propose a novel CNN architecture, called Dense-Res-Inception Net (DRINet), which addresses this challenging problem. The proposed DRINet consists of three blocks, namely a convolutional block with dense connections, a deconvolutional block with residual Inception modules, and an unpooling block. Our proposed architecture outperforms the U-Net in three different challenging applications, namely multi-class segmentation of cerebrospinal fluid (CSF) on brain CT images, multi-organ segmentation on abdominal CT images, multi-class brain tumour segmentation on MR images

Empirical Evaluation of the Parallel Distribution Sweeping Framework on Multicore Architectures

In this paper, we perform an empirical evaluation of the Parallel External Memory (PEM) model in the context of geometric problems. In particular, we implement the parallel distribution sweeping framework of Ajwani, Sitchinava and Zeh to solve batched 1-dimensional stabbing max problem. While modern processors consist of sophisticated memory systems (multiple levels of caches, set associativity, TLB, prefetching), we empirically show that algorithms designed in simple models, that focus on minimizing the I/O transfers between shared memory and single level cache, can lead to efficient software on current multicore architectures. Our implementation exhibits significantly fewer accesses to slow DRAM and, therefore, outperforms traditional approaches based on plane sweep and two-way divide and conquer.Comment: Longer version of ESA'13 pape

Bristlebots and other friends. A progression of Epistemic insight workshops using small things to ask big questions

Small, handmade and inexpensive robots can help students across a range of ages unpack and explore big questions around the nature of life, curiosity and creativity. This is an introduction to a series of workshops where students learn how to frame and investigate different types of questions including big questions that bridge science, religion, computing and the wider humanities. The first workshop, aimed at upper KS2 looks at the ideas of what we mean by life and to be alive. The second workshop builds on this and asks, ‘can a robot have a sense of curiosity?’ What would a robot need to have a sense of curiosity, what do we need across a range of subject domains. The third workshop takes this further and helps KS3/4 students to ask questions about what it means to be creative, would a robot make a good friend and our we, ourselves, programmed by the society that we grow up in? The workshops are a part of wider activities delivered across primary, secondary, ITE and outreach activities by the LASAR team accompanied by research informing development of epistemic insight in children and young people and equipping them with curiosity, analytical and critical skills to understand current global problems and answer Big Questions

Wide learning: Using an ensemble of biologically-plausible spiking neural networks for unsupervised parallel classification of spatio-temporal patterns

Spiking neural networks have been previously used to perform tasks such as object recognition without supervision. One of the concerns relating to the spiking neural networks is their speed of operation and the number of iterations necessary to train and use the network. Here, we propose a biologically plausible model of a spiking neural network which is used in multiple, separately trained copies to process subsets of data in parallel. This ensemble of networks is tested by applying it to the task of unsupervised classification of spatio-temporal patterns. Results show that despite different starting weights and independent training, the networks produce highly similar spiking patterns in response to the same class of inputs, enabling classification with fast training time

Evaluation of a collaborative photography workshop using the iPad 2 as an accessible technology for participants who are blind, visually impaired and sighted working collaboratively

A workshop using iPads to train photographers who are blind, visually impaired and sighted is evaluated using a model of inclusive technical capital. It was hypothesized that all participants would find iPad apps accessible. It was found that iPads were good introductory devices, but experienced participants who are blind and sighted still preferred specialized cameras

A functional analysis of two transdiagnostic, emotion-focused interventions on nonsuicidal self-injury

OBJECTIVE: Nonsuicidal self-injury (NSSI) is prevalent and associated with clinically significant consequences. Developing time-efficient and cost-effective interventions for NSSI has proven difficult given that the critical components for NSSI treatment remain largely unknown. The aim of this study was to examine the specific effects of mindful emotion awareness training and cognitive reappraisal, 2 transdiagnostic treatment strategies that purportedly address the functional processes thought to maintain self-injurious behavior, on NSSI urges and acts. METHOD: Using a counterbalanced, combined series (multiple baseline and data-driven phase change) aggregated single-case experimental design, the unique and combined impact of these 2 4-week interventions was evaluated among 10 diagnostically heterogeneous self-injuring adults. Ecological momentary assessment was used to provide daily ratings of NSSI urges and acts during all study phases. RESULTS: Eight of 10 participants demonstrated clinically meaningful reductions in NSSI; 6 participants responded to 1 intervention alone, whereas 2 participants responded after the addition of the alternative intervention. Group analyses indicated statistically significant overall effects of study phase on NSSI, with fewer NSSI urges and acts occurring after the interventions were introduced. The interventions were also associated with moderate to large reductions in self-reported levels of anxiety and depression, and large improvements in mindful emotion awareness and cognitive reappraisal skills. CONCLUSIONS: Findings suggest that brief mindful emotion awareness and cognitive reappraisal interventions can lead to reductions in NSSI urges and acts. Transdiagnostic, emotion-focused therapeutic strategies delivered in time-limited formats may serve as practical yet powerful treatment approaches, especially for lower-risk self-injuring individuals.Dr. Barlow receives royalties from Oxford University Press, Guilford Publications Inc., Cengage Learning, and Pearson Publishing. Grant monies for various projects come from the National Institute of Mental Health (F31MH100761), the National Institute of Alcohol and Alcohol Abuse, and Colciencias (Government of Columbia Initiative for Science, Technology, and Health Innovation). Consulting and honoraria during the past several years have come from the Agency for Healthcare Research and Quality, the Foundation for Informed Medical Decision Making, the Department of Defense, the Renfrew Center, the Chinese University of Hong Kong, Universidad Catolica de Santa Maria (Arequipa, Peru), New Zealand Psychological Association, Hebrew University of Jerusalem, Mayo Clinic, and various American Universities. (F31MH100761 - National Institute of Mental Health; National Institute of Alcohol and Alcohol Abuse; Colciencias (Government of Columbia Initiative for Science, Technology, and Health Innovation))Accepted manuscrip

Sleep and daytime sleepiness in methylphenidate medicated and un-medicated children with attention-deficit/hyperactivity disorder (ADHD)

Objective: Excessive daytime sleepiness due to any cause can result in various symptoms similar to those used for the diagnosis of attention deficit/hyperactivity disorder (ADHD). A common treatment for children diagnosed with ADHD is methylphenidate which is also used to treat excessive daytime sleepiness. This paper reports on a study which compared the perceived levels of daytime sleepiness and prevalence of sleep disorders in medicated and un-medicated children with ADHD. Method: The sample consisted of two matched groups of children who had been formally diagnosed with ADHD. One group (n=12) was taking immediate release methylphenidate twice daily, while the other group (n=11) had never, and were not currently, taking any medication. The two groups, as well as their parents, rated their levels of daytime sleepiness at three points in a single day. Results: Significantly higher levels of daytime sleepiness were reported by the parents of the un-medicated children between the hours of 13:00 and 15:00, compared to the medicated children. The medicated children became increasingly sleepier from the first to the second measurement in both the morning and afternoon. There was no significant difference in the number of sleep disorders/disruptions reported by the parents of either group. Conclusion: In a group of children with ADHD taking methylphenidate, there was a significant increase in  sleepiness a few hours after taking the medication, which may then have a significant impact on their learning. The data also imply that part of the mechanism of action of methylphenidate effects in these children may be by reduction of daytime sleepiness.Keywords: Attention Deficit Disorder with hyperactivity; Methylphenidate; Disorders of excessive somnolenc

STEM Careers Awareness Timelines Attitudes and ambitions towards science, technology, engineering and maths (STEM at Key Stage 3)

The importance of science, technology, engineering and maths (STEM) expertise to the UK economy is generally accepted as key to maintaining our international competitiveness. This importance was noted in the 2002 review by Sir Gareth Roberts, ‘Set for Success: The supply of people with science, technology, engineering and mathematics skills’. This report stemmed from the, ‘Government’s concern that the supply of high-quality scientists and engineers should not constrain the UK’s future research and development (R&D) and innovation performance.’ (Roberts, 2002, p.1