Content–based multimedia retrieval has been an active research domain since the mid 1990s. In the medical domain visual retrieval started later and has mostly remained a research instrument and less a clinical tool, even though a few tools for retrieval are employed in clinical work. The limited size of data sets due to privacy constraints is often mentioned as a reason for these limitations. Nevertheless, much work has been done in medical visual information retrieval, including the availability of increasingly large data sets and scientific challenges. Annotated data sets and clinical data for the images have now become available and can be combined for multi– modal retrieval. Much has been learned on user behavior and application scenarios. This text is motivated by the advances in medical image analysis and the availability of more public data large data sets that often include clinical data that can be combined for multimodal retrieval based on the experience available in the multimedia community. This text is a systematic review of recent work (concentrating on the period between 2011-2017) on content–based multi–modal retrieval and image understanding in the medical domain, where image understanding includes techniques such as detection, localization, and classification for leveraging visual content. The main conferences in the field are screened for relevant articles and these are presented in a structured way, identifying current limitations and areas where work is still much required. Objective of the work is to summarize the current state of research for multimedia researchers not working in the medical field. It provides ways to get data sets and identify promising research directions. The text highlights the areas of advances in the past six years and particularly a trend to use larger scale training data sets as well as deep learning approaches that can replace or complement hand–crafted feature extraction. Using images alone will likely only work in limited sub domains but combining multiple sources of data for multi–modal retrieval has the biggest chances of success, particularly for clinical impact. Future fields of research are identified in the text, as there is a high research potential in the medical multimedia domain.
This chapter clarifies the important aspects of biomedical texture analysis under the general framework introduced in Chapter 1. It was proposed that any approach can be character-ized as the combination of local texture operators and regional aggregation functions. The type of scale and directional information that can or cannot be modeled by categories of texture processing methods is revealed through theoretic analyses and experimental valida-tions. Several key aspects are found to be commonly overlooked in the literature and are highlighted. First, we demonstrate the risk of using regions of interest for aggregation that are regrouping tissue types of different natures. Second, a detailed study of the type of directional information important for biomedical texture characterization suggests that fun-damental properties lie in the local organization of image directions. In addition, it was found that most approaches cannot efficiently characterize the latter, and even fewer can do it with invariance to local rotations. We conclude by deriving novel comparison axes to evalu-ate the relevance of biomedical texture analysis methods in a specific medical or biological applicative context.
There is a growing interest in the treatment of animals used in the tourism industry, yet the academic literature on the animal welfare consideration exhibited by tourists is limited. This study seeks to identify if demographic differences amongst tourists engaging in animal-based tourism influence the importance they attribute to the ethical treatment of those animals. A statistical analysis of 136 completed questionnaires demonstrates statistically significant difference in animal welfare concerns between Asian and Western tourists. Suggestions for further research and managerial implications emanating from the findings conclude the paper.
The integration of autonomous vehicles (AVs) onto public roads presents both technical and social challenges. Public understanding and acceptance of AVs requires engagement with people who live in, work at or visit cities where they are deployed on public road networks. We investigate the impact of one of the first placements of AV passenger transport on public roadways: the Sion <<SmartShuttle>>. This late-breaking research presents preliminary results from interviews with local shopkeepers, residents, pedestrians and drivers to understand their attitudes and opinions of the shuttle. We also discuss video-based fieldwork that demonstrates how drivers negotiate next moves with one another through their windscreens using embodied signals such as gestures, lip-reading, and head nods to coordinate and manage a traffic situation. Finally, we consider the implications for how fully autonomous vehicles might be designed to take into account the subtle negotiations that road users engage in to coordinate with one another.
Among the proposed opportunistic content sharing services, Floating Content (FC) is of special interest for the vehicular environment, not only for cellular traffic offloading, but also as a natural communication paradigm for location-based context-aware vehicular applications. Existing results on the performance of vehicular FC have focused on content persistence, without addressing the key issues of the effectiveness with which content is replicated and made available, and of what are the conditions which enable acceptable FC performance in the vehicular environment. This work presents a first analytical model of FC performance in vehicular networks in urban settings. It is based on a variation of the random waypoint (RWP) mobility model, and it does not require a model of road grid geometry for its parametrization. We validate our model extensively, through numerical simulations on real-world traces, showing its accuracy on a variety of mobility patterns and traffic conditions. Through analysis and simulations, we show the feasibility of the FC paradigm in realistic urban settings over a wide range of traffic conditions.
Theprocessesofradiomicsconsistofimage-basedpersonalizedtumorphenotypingforpre-cision medicine. They complement slow, costly and invasive molecular analysis of tumoral tissue. Whereastherelevanceofalargevarietyofquantitativeimagingbiomarkershasbeen demonstrated for various cancer types, most studies were based on 2D image analysis of relatively small patient cohorts. In this work, we propose an online tool for automatically ex-tracting 3D state-of-the-art quantitative imaging features from large batches of patients. The developed platform is called QuantImage and can be accessed from any web browser. Its use is straightforward and can be further parameterized for refined analyses. It relies on a robust 3D processing pipeline allowing normalization across patients and imaging protocols. Theusercansimplydrag-and-dropalargezipfilecontainingallimagedataforabatchofpa-tients and the platform returns a spreadsheet with the set of quantitative features extracted for each patient. It is expected to enable high-throughput reproducible research and the validation of radiomics imaging parameters to shape the future of non-invasive personalized medicine.
Object detection and recognition algorithms usually require large, annotated training sets. The creation of such datasets requires expensive manual annotation. Eye tracking can help in the annotation procedure. Humans use vision constantly to explore the environment and plan motor actions, such as grasping an object. In this paper we investigate the possibility to semi-automatically train object recognition with eye tracking, accelerometer in scene camera data, learning from the natural hand-eye coordination of humans. Our approach involves three steps. First, sensor data are recorded using eye tracking glasses that are used in combination with accelerometers and surface electromyography that are usually applied when controlling prosthetic hands. Second, a set of patches are extracted automatically from the scene camera data while grasping an object. Third, a convolutional neural network is trained and tested using the extracted patches. Results show that the parameters of eye-hand coordination can be used to train an object recognition system semi-automatically. These can be exploited with proper sensors to fine-tune a convolutional neural network for object detection and recognition. This approach opens interesting options to train computer vision and multi-modal data integration systems and lays the foundations for future applications in robotics. In particular, this work targets the improvement of prosthetic hands by recognizing the objects that a person may wish to use. However, the approach can easily be generalized.
Techniques originating from the Internet of Things (IoT) and Cyber-Physical Systems (CPS) areas have extensively been applied to develop intelligent and pervasive systems such as assistive monitoring, feedback in telerehabilitation, energy management, and negotiation. Those application do-mains particularly include three major characteristics: intel-ligence, autonomy and real-time behavior. Multi-Agent Sys-tems (MAS) are one of the major technological paradigms that are used to implement such systems. However, they mainly address the first two characteristics, but miss to com-ply with strict timing constraints. The timing compliance is crucial for safety-critical applications operating in domains such as healthcare and automotive. The main reasons for this lack of real-time satisfiability in MAS originate from cur-rent theories, standards, and technological implementations. In particular, internal agent schedulers, communication mid-dlewares, and negotiation protocols have been identified as co-factors inhibiting the real-time compliance. This paper provides an analysis of such MAS components and pave the road for achieving the MAS compliance with strict timing constraints, thus fostering reliability and predictability.
