Learning from sparse labels is a challenge commonplace in the medical domain. This is due to numerous factors, such as annotation cost, and is especially true for newly introduced tasks. When dense pixel-level annotations are needed, this becomes even more unfeasible. However, being able to learn from just a few annotations at the pixel-level, while extremely difficult and underutilized, can drive progress in studies where perfect annotations are not immediately available. This work tackles the challenge of learning the dense prediction task of keypoint localization from a few point annotations in the context of 2d carcinosis keypoint localization from laparoscopic video frames for diagnostic planning of advanced ovarian cancer patients. To enable this, we formulate the problem as a sparse heatmap regression from a few point annotations per image and propose a new loss function, called Crag and Tail loss, for efficient learning. Our proposed loss function effectively leverages positive sparse labels while minimizing the impact of false negatives or missed annotations. Through an extensive ablation study, we demonstrate the effectiveness of our approach in achieving accurate dense localization of carcinosis keypoints, highlighting its potential to advance research in scenarios where dense annotations are challenging to obtain.

PongBot is an artificial intelligence-powered tennis robot. American tennis star Danielle Collins on Tuesday defended her outburst toward a cameraman during a tournament last week. Collins' incident occurred at the Internationaux de Strasbourg against Emma Raducanu. During a changeover, she told the cameraman to keep their distance as she refilled her water bottle. She said the cameraman was acting "wildly inappropriate."

While laparoscopic liver resection is less prone to complications and maintains patient outcomes compared to traditional open surgery, its complexity hinders widespread adoption due to challenges in representing the liver's internal structure. Laparoscopic intraoperative ultrasound offers efficient, cost-effective and radiation-free guidance. Our objective is to aid physicians in identifying internal liver structures using laparoscopic intraoperative ultrasound. We propose a patient-specific approach using preoperative 3D ultrasound liver volume to train a deep learning model for real-time identification of portal tree and branch structures. Our personalized AI model, validated on ex vivo swine livers, achieved superior precision (0.95) and recall (0.93) compared to surgeons, laying groundwork for precise vessel identification in ultrasound-based liver resection. Its adaptability and potential clinical impact promise to advance surgical interventions and improve patient care.

Clinnova, a collaborative initiative involving France, Germany, Switzerland, and Luxembourg, is dedicated to unlocking the power of precision medicine through data federation, standardization, and interoperability. This European Greater Region initiative seeks to create an interoperable European standard using artificial intelligence (AI) and data science to enhance healthcare outcomes and efficiency. Key components include multidisciplinary research centers, a federated biobanking strategy, a digital health innovation platform, and a federated AI strategy. It targets inflammatory bowel disease, rheumatoid diseases, and multiple sclerosis (MS), emphasizing data quality to develop AI algorithms for personalized treatment and translational research. The IHU Strasbourg (Institute of Minimal-invasive Surgery) has the lead in this initiative to develop the federated learning (FL) proof of concept (POC) that will serve as a foundation for advancing AI in healthcare. At its core, Clinnova-MS aims to enhance MS patient care by using FL to develop more accurate models that detect disease progression, guide interventions, and validate digital biomarkers across multiple sites. This technical report presents insights and key takeaways from the first cross-border federated POC on MS segmentation of MRI images within the Clinnova framework. While our work marks a significant milestone in advancing MS segmentation through cross-border collaboration, it also underscores the importance of addressing technical, logistical, and ethical considerations to realize the full potential of FL in healthcare settings.

We present a framework for constraining the automatic sequential generation of equations to obey the rules of dimensional analysis by construction. Combining this approach with reinforcement learning, we built $\Phi$-SO, a Physical Symbolic Optimization method for recovering analytical functions from physical data leveraging units constraints. Our symbolic regression algorithm achieves state-of-the-art results in contexts in which variables and constants have known physical units, outperforming all other methods on SRBench's Feynman benchmark in the presence of noise (exceeding 0.1%) and showing resilience even in the presence of significant (10%) levels of noise.

New large observational surveys such as Gaia are leading us into an era of data abundance, offering unprecedented opportunities to discover new physical laws through the power of machine learning. Here we present an end-to-end strategy for recovering a free-form analytical potential from a mere snapshot of stellar positions and velocities. First we show how auto-differentiation can be used to capture an agnostic map of the gravitational potential and its underlying dark matter distribution in the form of a neural network. However, in the context of physics, neural networks are both a plague and a blessing as they are extremely flexible for modeling physical systems but largely consist in non-interpretable black boxes. Therefore, in addition, we show how a complementary symbolic regression approach can be used to open up this neural network into a physically meaningful expression. We demonstrate our strategy by recovering the potential of a toy isochrone system.

Mediation choreography workshop is a practice and training session to improve bodily confidence, comfort, awareness and dialogue during social interactions focused on the self-presentation development in children and youngsters. The workshop teaches non-verbal mediation and dialogue: Body postures, Expressions and Form aggregations inspired from the museum sculptures. During the workshop, mediation choreography experts help young participants to improvise and feel the quality of our bodily perception, performance and presentation. Disclaimer: The description of Mediation choreography is my understanding as a foreigner in the French city, Strasbourg. I participated in the workshop among native speakers.

CEIPI is pleased to announce the offering of the 3rd edition of the Advanced Training Program on "Artificial Intelligence and Intellectual Property" that will take place in Strasbourg from 23 to 25 April 2020. This new training follows the very successful editions of past years, gathering a high number of professionals coming from almost all the European countries, and as far as Brazil, Canada, United States, China, India, Malaysia and Japan, and including senior officials from renowned institutions. Artificial Intelligence (AI) and robots have been the subject of science fiction for some time. That fictional future is now a present reality. The regulation of AI's activities is set to become a primary policy issue.

The impact of the development of computer science on the knowledge of law is phenomenal and fundamental. Yet, few lawyers have the expertise to understand the impact of new algorithmic methods in their practice. The objectives of the training are twofold: the first is to transfer knowledge and skills in this high-tech sector, while the second is to provide technical training to lawyers. The university degree "Artificial Intelligence and Intellectual Property" has, on the one hand, a goal to remedy this lack in the field of intellectual property rights. Indeed, if there are many training courses on the digital and the law, none sufficiently understates the new issues of artificial intelligence in the field of intellectual property rights, in order to understand and control the issues of protection of these new types of creation, their usefulness to the implementation of rights, as well as their technical and economic environment.

These "breakfasts" are intended to be a convivial place for exchange with the invited expert, but also between the Council of Europe community and Strasbourg academics interested in this subject. The first meeting will take place on Thursday 27 June 2019 (8h30 - 10h30) at the Maison interuniversitaire des sciences de l'Homme (MISHA), 5 allée du Général Rouvillois (Strasbourg) - Tram E / Stop Observatoire. Frédéric Wickert, an entrepreneur and expert with the Council of Europe, will take part in this first exercise on the theme: the different faces of AI.