Deep neural networks (DNNs) have achieved outstanding performance and broad implementation in computer vision tasks such as classification, denoising, segmentation and image synthesis. However, DNN-based models and algorithms have seen limited adaptation and development within radiomics which aim to improve diagnosis or prognosis of cancer. Traditionally, medical practitioners have used expert-derived features such as intensity, shape, textual, and others. We hypothesize that, despite the potential of DNNs to improve oncological classification performances in radiomics, a lack of interpretability of such models prevents their broad utilization, performance, and generalizability. Therefore, the INFORM consortium proposes to investigate explainable artificial intelligence (XAI) with a dual aim of building high performance DNN-based classifiers and developing novel interpretability techniques for radiomics. First, in order to overcome the limited data typically available in radomic studies, we will investigate Monte Carlo methods and generative adversarial networks (GAN) for realistic simulation that can aid building and training DNN architectures. Second, we tackle the interpretability of DNN-based feature engineering and latent variable modeling with innovative developments of saliency maps and related visualization techniques. Both supervised and unsupervised learning will be used to generate features, which can be interpreted in terms of input pixels and expert-derived features. Third, we propose to build explainable AI models that incorporate both expert-derived and DNN-based features. By quantitatively understanding the interplay between expert-derived and DNN-based features, our models will be readily understood and translated into medical applications. Fourth, evaluation will be carried out by clinical collaborators with a focus on lung, cervical and rectal cancer. These proposed DNN models, specifically developed to reveal their innerworkings, will leverage the robustness and trustworthiness of expert-derived features that medical practitioners are familiar with, while providing quantitative and visual feedback. Overall, our methodological research will advance interpretability of feature engineering, generative models, and DNN classifiers with applications in radiomics and broad medical imaging. With this project we aim at maximizing the impact on the patient management of ML and DL techniques by developing novel methods to facilitate training of decision-aid systems for clinical treatment strategies optimization. The methodological approaches we propose in this specific area will play a major role in facilitating the acceptability of DL-based decision-aid systems relying on medical imaging for oncology. The proposed validated predictive models in various cancer types within the context of this project might subsequently be used to drive future prospective clinical studies in which patients could be offered alternative treatment strategies based on the results of these predictive models. Such a clinical and social potential is further enhanced by the public-private collaboration proposed in this project, where the developed methodologies will find their way in products. The multidisciplinarity of INFORM is key to meet the target challenges and achieve the proposed goals. All partners have their individual world-leading qualifications and additional scientific expertise providing all the prerequisites for the efficient implementation of INFORM’s approach. The successful implementation of this project will have a large and prolonged impact both in the Medical/Oncology and the Computing/ Artificial Intelligence field of predictive radiomics model, as well as the same methodology could be extended to other diagnostic and therapeutic medical applications.

The Quantum Secure Networks Partnership (QSNP) project aims at creating a sustainable European ecosystem in quantum cryptography and communication. A majority of its partners, which include world-leading academic groups, research and technology organizations (RTOs), quantum component and system spin-offs, cybersecurity providers, integrators, and telecommunication operators, were members of the European Quantum Flagship projects CIVIQ, UNIQORN and QRANGE. QSNP thus gathers the know-how and expertise from all technology development phases, ranging from innovative designs to development of prototypes for field trials. QSNP is structured around three main Science and Technology (ST) pillars. The first two pillars, “Next Generation Protocols” and “Integration”, focus on frontier research and innovation, led mostly by academic partners and RTOs. The third ST pillar “Use cases and Applications” aims at expanding the industrial and economic impact of QSN technologies and is mostly driven by companies. In order to achieve the specific objectives within each pillar and ensure that know-how transfer and synergy between them are coherent and effective, QSNP has established ST activities corresponding to the three main layers of the technology value chain, “Components and Systems”, “Networks” and “Cryptography and Security”. This framework will allow achieving the ultimate objective of developing quantum communication technology for critical European infrastructures, such as EuroQCI, as well as for the private information and communication technology (ICT) sectors. QSNP will contribute to the European sovereignty in quantum technology for cybersecurity. Additionally, it will generate significant economic benefits to the whole society, including training new generations of scientists and engineers, as well as creating high-tech jobs in the rapidly growing quantum industry.

Abortion laws are the crux of human rights diversity today. Abortion laws evidence best how differently human rights meanings are construed in various local settings. However, we know very little about how this diversity is generated in practice. This project will scrutinize the communication processes that use human rights as arguments to change abortion laws. We will contrast abortion debates from the last ten years in pairs of countries that represent three regional human rights systems: Mozambique and Senegal (the African Union), Poland and Ireland (the Council of Europe), and Argentina and Honduras (the Organization of American States). These debates show the ambivalence of human rights: they were used successfully to argue both for more liberal and more restrictive abortion laws. To explain this ambivalence, we will apply concepts of argumentative architecture and involvement patterns, coined by the PI as part of her figurational sociology of law, based on Norbert Elias’s theory of the process of civilization. Using a mixed-methods approach that combines qualitative sociology, legal analysis, and corpus linguistics, we will offer a multi-dimensional model for a globally comparative, interdisciplinary socio-legal study of human rights. We will study the structure, composition, and embedding of arguments, along with group perspectives, emotions, and circles of identification of arguing actors so as to arrive at a heat map that will show the distribution of involvement in argumentative architectures. By constructing a global meta-typology of argumentative architectures and involvement patterns in abortion debates, we will explore the integrative, civilizing potential of human rights and identify the centrifugal forces in human rights figuration that comprise the local, regional, and global levels. Finally, we will revisit the role of human rights as a universal toolbox for ideologies in order to plead their conditional rehabilitation.