Deep learning-based image analysis in muscle histopathology using photo-realistic synthetic data.

Background: Artificial intelligence (AI), specifically Deep learning (DL), has revolutionized biomedical image analysis, but its efficacy is limited by the need for representative, high-quality large datasets with manual annotations. While latest research on synthetic data using AI-based generative models has shown promising results to tackle this problem, several challenges such as lack of interpretability and need for vast amounts of real data remain. This study aims to introduce a new approach-SYNTA-for the generation of photo-realistic synthetic biomedical image data to address the challenges associated with state-of-the art generative models and DL-based image analysis.

Incorporating local researchers' and decision makers' preferences for groundwater resources management in a spatial multi-voiced decision model.

Water is a crucial and highly sensitive political issue. The overexploitation of groundwater resources and deterioration of water quality caused by pollution as well as the deficiencies of the existing infrastructure require immediate attention. The main goal of this paper is to carry out a multi-voiced multicriteria analysis that integrates the preferences of local researchers and decision makers in a process aimed at prioritizing watersheds that require the implementation of groundwater management instruments.