MILD-Net: Minimal Information Loss Dilated Network for Gland Instance Segmentation in Colon Histology Images

The analysis of glandular morphology within colon histopathology images is an important step in determining the grade of colon cancer. Despite the importance of this task, manual segmentation is laborious, time-consuming and can suffer from subjectivity among pathologists. The rise of computational pathology has led to the development of automated methods for gland segmentation that aim to overcome the challenges of manual segmentation. However, this task is non-trivial due to the large variability in glandular appearance and the difficulty in differentiating between certain glandular and non-glandular histological structures. Furthermore, a measure of uncertainty is essential for diagnostic decision making. To address these challenges, we propose a fully convolutional neural network that counters the loss of information caused by max-pooling by re-introducing the original image at multiple points within the network. We also use atrous spatial pyramid pooling with varying dilation rates for preserving the resolution and multi-level aggregation. To incorporate uncertainty, we introduce random transformations during test time for an enhanced segmentation result that simultaneously generates an uncertainty map, highlighting areas of ambiguity. We show that this map can be used to define a metric for disregarding predictions with high uncertainty. The proposed network achieves state-of-the-art performance on the GlaS challenge dataset and on a second independent colorectal adenocarcinoma dataset. In addition, we perform gland instance segmentation on whole-slide images from two further datasets to highlight the generalisability of our method. As an extension, we introduce MILD-Net+ for simultaneous gland and lumen segmentation, to increase the diagnostic power of the network.Comment: Initial version published at Medical Imaging with Deep Learning (MIDL) 201

Diagnostics Of Disks Around Hot Stars

We discuss three different observational diagnostics related to disks around hot stars: absorption line determinations of rotational velocities of Be stars; polarization diagnostics of circumstellar disks; and X-ray line diagnostics of one specific magnetized hot star, theta(1) Ori C. Some common themes that emerge from these studies include (a) the benefits of having a specific physical model as a framework for interpreting diagnostic data; (b) the importance of combining several different types of observational diagnostics of the same objects; and (c) that while there is often the need to reinterpret traditional diagnostics in light of new theoretical advances, there are many new and powerful diagnostics that are, or will soon be, available for the study of disks around hot stars

Sexual Attraction Toward Clients, Use of Supervision, and Prior Training: A Qualitative Study of Predoctoral Psychology Interns

Interviews were conducted with 13 predoctoral psychology interns about an experience of sexual attraction toward a client, use of supervision to address the sexual attraction, and prior training regarding sexual attraction. Results indicated that sexual attraction to clients consisted of physical and interpersonal aspects. Therapists believed they were more invested and attentive than usual to clients to whom they were sexually attracted, and they indicated that sexual attraction created distance, distraction, and loss of objectivity. In terms of supervision, only half of the participants disclosed their sexual attraction to supervisors, and supervisors seldom initiated the discussion. Furthermore, trainees found it helpful when supervisors normalized the sexual attraction and provided the opportunity to explore feelings in supervision. Finally, trainees believed their training programs did not adequately address therapist sexual attraction

Dental care professionals and child protection: case scenarios and discussions

Any concerns about paediatric patients in general dental practice can be stressful for all involved. Barriers to the reporting of concerns by dental teams are known to exist. Anything that can help ease those situations can only be beneficial. In this article we look at three scenarios that could arise which I am often asked about during teaching and training sessions on safeguarding and child protection for dental teams. They can be discussed at team meetings and training, so that if they are ever to happen for real, everyone will know exactly what to do. This article cannot be completely prescriptive as there will be local variations, but it gives general guidance on issues raised by the scenarios. If you already have a child protection policy in your practice, make sure you know what it says; and if you don't this article will point the way to further resources for developing one

Detecting and Refactoring Operational Smells within the Domain Name System

The Domain Name System (DNS) is one of the most important components of the Internet infrastructure. DNS relies on a delegation-based architecture, where resolution of names to their IP addresses requires resolving the names of the servers responsible for those names. The recursive structures of the inter dependencies that exist between name servers associated with each zone are called dependency graphs. System administrators' operational decisions have far reaching effects on the DNSs qualities. They need to be soundly made to create a balance between the availability, security and resilience of the system. We utilize dependency graphs to identify, detect and catalogue operational bad smells. Our method deals with smells on a high-level of abstraction using a consistent taxonomy and reusable vocabulary, defined by a DNS Operational Model. The method will be used to build a diagnostic advisory tool that will detect configuration changes that might decrease the robustness or security posture of domain names before they become into production.Comment: In Proceedings GaM 2015, arXiv:1504.0244

Centrality anomalies in complex networks as a result of model over-simplification

Tremendous advances have been made in our understanding of the properties and evolution of complex networks. These advances were initially driven by information-poor empirical networks and theoretical analysis of unweighted and undirected graphs. Recently, information-rich empirical data complex networks supported the development of more sophisticated models that include edge directionality and weight properties, and multiple layers. Many studies still focus on unweighted undirected description of networks, prompting an essential question: how to identify when a model is simpler than it must be? Here, we argue that the presence of centrality anomalies in complex networks is a result of model over-simplification. Specifically, we investigate the well-known anomaly in betweenness centrality for transportation networks, according to which highly connected nodes are not necessarily the most central. Using a broad class of network models with weights and spatial constraints and four large data sets of transportation networks, we show that the unweighted projection of the structure of these networks can exhibit a significant fraction of anomalous nodes compared to a random null model. However, the weighted projection of these networks, compared with an appropriated null model, significantly reduces the fraction of anomalies observed, suggesting that centrality anomalies are a symptom of model over-simplification. Because lack of information-rich data is a common challenge when dealing with complex networks and can cause anomalies that misestimate the role of nodes in the system, we argue that sufficiently sophisticated models be used when anomalies are detected.Comment: 14 pages, including 9 figures. APS style. Accepted for publication in New Journal of Physic