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The Schengen Area is one of the pillars of the European project. But it has been affected by several difficulties: the serious consequences of the global economic and financial crisis (2008-2018), growing concerns over external migratory pressure and the question of enlargement, fears of social dumping and, since March 2020, the COVID-19 crisis. Identifying these obstacles is vital so that pragmatic solutions can be found without jeopardising the founding principle.

Our working hypothesis is that key factors in COVID-19 imaging are the available imaging data and their label noise and confounders, rather than network architectures per se. Thus, we applied existing state-of-the-art convolution neural network frameworks based on the U-Net architecture, namely nnU-Net [3], and focused on leveraging the available training data. We did not apply any pre-training nor modi ed the network architecture. First, we enriched training information by generating two additional labels for lung and body area. Lung labels were created with a public available lung segmentation network and weak body labels were generated by thresholding. Subsequently, we trained three di erent multi-class networks: 2-label (original background and lesion labels), 3-label (additional lung label) and 4-label (additional lung and body label). The 3-label obtained the best single network performance in internal cross-validation (Dice-Score 0.756) and on the leaderboard (Dice- Score 0.755, Haussdor 95-Score 57.5). To improve robustness, we created a weighted ensemble of all three models, with calibrated weights to optimise the ranking in Dice-Score. This ensemble achieved a slight performance gain in internal cross-validation (Dice-Score 0.760). On the validation set leaderboard, it improved our Dice-Score to 0.768 and Haussdor 95- Score to 54.8. It ranked 3rd in phase I according to mean Dice-Score. Adding unlabelled data from the public TCIA dataset in a student-teacher manner signi cantly improved our internal validation score (Dice-Score of 0.770). However, we noticed partial overlap between our additional training data (although not human-labelled) and nal test data and therefore submitted the ensemble without additional data, to yield realistic assessments.

Although the idea of Europe dates back to ancient times and was crystallised in the Enlightenment, the plan for European unification emerged in the second half of the 20th century as a consequence of an economic process based on a single market and a single currency. European integration is therefore a recent chapter in the history of Europe, one which has been written before our very eyes, but it remains fragmented into disparate national histories. In the 21st century, those writing the history of Europe find themselves confronted with a threefold challenge: they must meet the demands of the digital age, adjust to the paradigm shift within the historical discipline and navigate the geopolitical upheavals that the continent has been experiencing since 1989 (the fall of communism; the enlargement of the European Union; the many crises the EU has faced, including Brexit; the divide between institutions and citizens; the socio-economic consequences of the global crisis, including the COVID-19 health crisis; the new nature of transatlantic relations, etc.).