The holistic perspective of the INCISIVE project : artificial intelligence in screening mammography

Finding new ways to cost-effectively facilitate population screening and improve cancer diagnoses at an early stage supported by data-driven AI models provides unprecedented opportunities to reduce cancer related mortality. This work presents the INCISIVE project initiative towards enhancing AI solutions for health imaging by unifying, harmonizing, and securely sharing scattered cancer-related data to ensure large datasets which are critically needed to develop and evaluate trustworthy AI models. The adopted solutions of the INCISIVE project have been outlined in terms of data collection, harmonization, data sharing, and federated data storage in compliance with legal, ethical, and FAIR principles. Experiences and examples feature breast cancer data integration and mammography collection, indicating the current progress, challenges, and future directions

WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM

Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments

Clear Cell Renal Cell Carcinoma 2021–2022

Clear cell renal cell carcinoma is currently one of the most interesting areas of study in oncology. Despite the advances made in this field, this tumor continues to be a health problem of major concern in Western societies, seriously affecting public health services. Several characteristics of this tumor make it an exciting meeting point for translational collaboration between clinicians and basic researchers. Clear cell renal cell carcinoma is a paradigmatic example of inter- and intra-tumor heterogeneity from morphological, immunohistochemical, and molecular viewpoints. This tumor is also a good example to investigate the complexity of tumor/tumor and tumor/environment relationships from an ecological perspective. A deeper identification of the varied internal tumor self-organization through the specialization of cell clones and subclones as local invaders and metastasizers, on one hand, and the interactions of specific subsets of tumor cells with the local host microenvironment, on the other, will significantly enrich our knowledge of this neoplasm. Clear cell renal cell carcinoma is also a paradigmatic test bench for antiangiogenic and immune checkpoint blockage therapies. The refinement of these therapeutic tools administered alone or in combination is a hot issue in oncology, and several international trials are underway

MS FT-2-2 7 Orthogonal polynomials and quadrature: Theory, computation, and applications

Quadrature rules find many applications in science and engineering. Their analysis is a classical area of applied mathematics and continues to attract considerable attention. This seminar brings together speakers with expertise in a large variety of quadrature rules. It is the aim of the seminar to provide an overview of recent developments in the analysis of quadrature rules. The computation of error estimates and novel applications also are described