‘Do you see what I see?’ Medical imaging: the interpretation of visual information

Röntgen's discovery of x-rays in 1895, gave to medicine the extraordinary benefit of being able to see inside the living body without surgery. Over time, technology has added to the sophistication of imaging processes in medicine and we now have a wide range of techniques at our disposal for the investigation and early detection of disease. But radiology deals with visual information; and like any information this requires interpretation. It is a practical field and medical images are used to make inferences about the state of peoples' health. These inferences are subject to the same variability and error as any decision-making process and so the criteria for the success of medical imaging are based not entirely on the images themselves but on the performance of the decision-makers. Research in the accuracy of medical imaging must draw on techniques from a wide range of disciplines including physics, psychology, computing, neuroscience and medicine in attempting to better understand the processes involved in visual decision-making in this context and to minimise diagnostic error

A reflective characterisation of occasional user

This work revisits established user classifications and aims to characterise a historically unspecified user category, the Occasional User (OU). Three user categories, novice, intermediate and expert, have dominated the work of user interface (UI) designers, researchers and educators for decades. These categories were created to conceptualise user's needs, strategies and goals around the 80s. Since then, UI paradigm shifts, such as direct manipulation and touch, along with other advances in technology, gave new access to people with little computer knowledge. This fact produced a diversification of the existing user categories not observed in the literature review of traditional classification of users. The findings of this work include a new characterisation of the occasional user, distinguished by user's uncertainty of repetitive use of an interface and little knowledge about its functioning. In addition, the specification of the OU, together with principles and recommendations will help UI community to informatively design for users without requiring a prospective use and previous knowledge of the UI. The OU is an essential type of user to apply user-centred design approach to understand the interaction with technology as universal, accessible and transparent for the user, independently of accumulated experience and technological era that users live in

Exploiting Deep Features for Remote Sensing Image Retrieval: A Systematic Investigation

Remote sensing (RS) image retrieval is of great significant for geological information mining. Over the past two decades, a large amount of research on this task has been carried out, which mainly focuses on the following three core issues: feature extraction, similarity metric and relevance feedback. Due to the complexity and multiformity of ground objects in high-resolution remote sensing (HRRS) images, there is still room for improvement in the current retrieval approaches. In this paper, we analyze the three core issues of RS image retrieval and provide a comprehensive review on existing methods. Furthermore, for the goal to advance the state-of-the-art in HRRS image retrieval, we focus on the feature extraction issue and delve how to use powerful deep representations to address this task. We conduct systematic investigation on evaluating correlative factors that may affect the performance of deep features. By optimizing each factor, we acquire remarkable retrieval results on publicly available HRRS datasets. Finally, we explain the experimental phenomenon in detail and draw conclusions according to our analysis. Our work can serve as a guiding role for the research of content-based RS image retrieval

Automated Satellite-Based Landslide Identification Product for Nepal

Landslide event inventories are a vital resource for landslide susceptibility and forecasting applications. However, landslide inventories can vary in accuracy, availability, and timeliness as a result of varying detection methods, reporting, and data availability. This study presents an approach to use publicly available satellite data and open source software to automate a landslide detection process called the Sudden Landslide Identification Product (SLIP). SLIP utilizes optical data from the Landsat 8 OLI sensor, elevation data from the Shuttle Radar Topography Mission (SRTM), and precipitation data from the Global Precipitation Measurement (GPM) mission to create a reproducible and spatially customizable landslide identification product. The SLIP software applies change detection algorithms to identify areas of new bare-earth exposures that may be landslide events. The study also presents a precipitation monitoring tool that runs alongside SLIP called the Detecting Real-time Increased Precipitation (DRIP) model that helps identify the timing of potential landslide events detected by SLIP. Using SLIP and DRIP together, landslide detection is improved by reducing problems related to accuracy, availability, and timeliness that are prevalent in the state-of-the-art of landslide detection. A case study and validation exercise was performed in Nepal for images acquired between 2014 and 2015. Preliminary validation results suggest 56% model accuracy, with errors of commission often resulting from newly cleared agricultural areas. These results suggest that SLIP is an important first attempt in an automated framework that can be used for medium resolution regional landslide detection, although it requires refinement before being fully realized as an operational tool

Sex By Deception

In this paper I will use sex by deception as a case study for highlighting some of the most tricky concepts around sexuality and moral psychology, including rape, consensual sex, sexual rights, sexual autonomy, sexual individuality, and disrespectful sex. I begin with a discussion of morally wrong sex as rooted in the breach of five sexual liberty rights that are derived from our fundamental human liberty rights: sexual self-possession, sexual autonomy, sexual individuality, sexual dignity and sexual privacy. I then argue (against the standard interpretation) that experimental findings in moral psychology show that the principle of respect for persons—a principle that grounds our human liberty rights—drives our intuitive moral judgments. In light of this discussion, I examine a puzzle about sex by deception—a puzzle which at first may seem to compel us to define 'rape' strictly in terms of force rather than sexual autonomy. I proceed by presenting an argument against the view that, as a rule, sex by deception undermines consent—a position held by prominent thinkers such as Philippe Patry (2001), Onora O’Neill (2003), Rubenfeld (2012), Tom Dougherty (2013a, 2013b), Joyce M. Short (2013), and Danielle Bromwich and Joseph Millum (2013, 2018). As we will see, sex following deception to increase your chances does not always constitute rape. Lying about your age, education, job, family background, marital status, or interest in a relationship, for example, does not make your sex partner incapable of consenting, which is to say that sex by deception need not be rape. I even go so far as to say that sex with another person that is facilitated by withholding information about having a venereal disease shouldn't be classified as rape. Although sex by deception doesn't compromise consent, it nonetheless violates the principle of respect for persons, not by vitiating sexual autonomy and compromising consent, but by failing to respect other sexual rights, such as our rights to sexual dignity, individuality, and privacy

Representation and fusion of heterogeneous fuzzy information in the 3D space for model-based structural recognition—Application to 3D brain imaging

AbstractWe present a novel approach to model-based pattern recognition where structural information and spatial relationships have a most important role. It is illustrated in the domain of 3D brain structure recognition using an anatomical atlas. Our approach performs segmentation and recognition of the scene simultaneously. The solution of the recognition task is progressive, processing successively different objects, and using different pieces of knowledge about the object and about relationships between objects. Therefore, the core of the approach is the knowledge representation part, and constitutes the main contribution of this paper. We make use of a spatial representation of each piece of information, as a spatial fuzzy set representing a constraint to be satisfied by the searched object, thanks in particular to fuzzy mathematical morphology operations. Fusion of these constraints allows us to select, segment and recognize the desired object

Learning in evolutionary environments

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