Chest X-ray images are useful for early COVID-19 diagnosis with the advantage that X-ray devices are already available in health centers and images are obtained immediately. Some datasets containing X-ray images with cases (pneumonia or COVID-19) and controls have been made available to develop machine-learning-based methods to aid in diagnosing the disease. However, these datasets are mainly composed of different sources coming from pre-COVID-19 datasets and COVID-19 datasets. Particularly, we have detected a significant bias in some of the released datasets used to train and test diagnostic systems, which might imply that the results published are optimistic and may overestimate the actual predictive capacity of the techniques proposed. In this paper, we analyze the existing bias in some commonly used datasets and propose a series of preliminary steps to carry out before the classic machine learning pipeline in order to detect possible biases, to avoid them if possible and to report results that are more representative of the actual predictive power of the methods under analysis.

Industrial inspection industry requires high precision, fast and reliable systems, where images play a central role. These systems are composed by several hardware and also cyber-physical componentes where complexity increases when multiple heterogeneous sensor inputs are combined. Our 3D industrial inspection scanner is able to reconstruct complete objects without occlusion with use of multiple sensors and actuators using a complex software architecture. Our system allows increasing the throughput by removing the bottleneck network issue, decreasing network data transfer using a new edge systems architecture that segments and optimizes image transferring. Also, this work presents the results of applying technology developed during the FitOptiVis European ECSEL project. FitOptiVis will provide a reference architecture supporting composability built on suitable component abstractions and embedded sensing, actuation and processing devices adhering to those abstractions. The reference architecture will support design portability, on-line multi-objective quality and resource management and run-time adaptation guaranteeing system constraints and requirements based on platform virtualization. The FitOptiVis project will be applied to design the new architecture of the new edge components and develop the runtime system monitoring.

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