EuroSDR research on state-of-the-art of automated generalisation in commercial software: main findings and conclusions

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Collaboration on an Ontology for Generalisation

workshopInternational audienceTo move beyond the current plateau in automated cartography we need greater sophistication in the process of selecting generalisation algorithms. This is particularly so in the context of machine comprehension. We also need to build on existing algorithm development instead of duplication. More broadly we need to model the geographical context that drives the selection, sequencing and degree of application of generalisation algorithms. We argue that a collaborative effort is required to create and share an ontology for cartographic generalisation focused on supporting the algorithm selection process. The benefits of developing a collective ontology will be the increased sharing of algorithms and support for on-demand mapping and generalisation web services

Artificial Intelligence in Radiotherapy Treatment Planning: Present and Future.

Treatment planning is an essential step of the radiotherapy workflow. It has become more sophisticated over the past couple of decades with the help of computer science, enabling planners to design highly complex radiotherapy plans to minimize the normal tissue damage while persevering sufficient tumor control. As a result, treatment planning has become more labor intensive, requiring hours or even days of planner effort to optimize an individual patient case in a trial-and-error fashion. More recently, artificial intelligence has been utilized to automate and improve various aspects of medical science. For radiotherapy treatment planning, many algorithms have been developed to better support planners. These algorithms focus on automating the planning process and/or optimizing dosimetric trade-offs, and they have already made great impact on improving treatment planning efficiency and plan quality consistency. In this review, the smart planning tools in current clinical use are summarized in 3 main categories: automated rule implementation and reasoning, modeling of prior knowledge in clinical practice, and multicriteria optimization. Novel artificial intelligence-based treatment planning applications, such as deep learning-based algorithms and emerging research directions, are also reviewed. Finally, the challenges of artificial intelligence-based treatment planning are discussed for future works

CartAGen: an Open Source Research Platform for Map Generalization

International audienceAutomatic map generalization is a complex task that is still a research problem and requires the development of research prototypes before being usable in productive map processes. In the meantime, reproducible research principles are becoming a standard. Publishing reproducible research means that researchers share their code and their data so that other researchers might be able to reproduce the published experiments, in order to check them, extend them, or compare them to their own experiments. Open source software is a key tool to share code and software, and CartAGen is the first open source research platform that tackles the overall map generalization problem: not only the building blocks that are generalization algorithms, but also methods to chain them, and spatial analysis tools necessary for data enrichment. This paper presents the CartAGen platform, its architecture and its components. The main component of the platform is the implementation of several multi-agent based models of the literature such as AGENT, CartACom, GAEL, CollaGen, or DIOGEN. The paper also explains and discusses different ways, as a researcher, to use or to contribute to CartAGen

Three-Dimensional Dose Prediction for Lung IMRT Patients with Deep Neural Networks: Robust Learning from Heterogeneous Beam Configurations

The use of neural networks to directly predict three-dimensional dose distributions for automatic planning is becoming popular. However, the existing methods only use patient anatomy as input and assume consistent beam configuration for all patients in the training database. The purpose of this work is to develop a more general model that, in addition to patient anatomy, also considers variable beam configurations, to achieve a more comprehensive automatic planning with a potentially easier clinical implementation, without the need of training specific models for different beam settings

Lessons Learned From Research on Multimedia Summarization

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The perceived quality of process discovery tools

Process discovery has seen a rise in popularity in the last decade for both researchers and businesses. Recent developments mainly focused on the power and the functionalities of the discovery algorithm. While continuous improvement of these functional aspects is very important, non-functional aspects such as visualization and usability are often overlooked. However, these aspects are considered valuable for end-users and play an important part in the experience of these end-users when working with a process discovery tool. A questionnaire has been sent out to give end-users the opportunity to voice their opinion on available process discovery tools and about the state of process discovery as a domain in general. The results of 66 respondents are presented and compared with the answers of 63 respondents that were contacted through one particular software vendor's employee and customer base (i.e., Celonis)