Rui Camacho

Abstract
Medical image analysis is crucial for the efficient diagnosis of many diseases. Typically, hospitals maintain vast repositories of images, which can be leveraged for various purposes, including research. However, access to such image collections is largely restricted to safeguard the privacy of the individuals whose images are being stored, as data protection concerns come into play. Recently, the development of solutions for Automated Medical Image Analysis has gained significant attention, with Deep Learning being one solution that has achieved remarkable results in this area. One promising approach for medical image analysis is Federated Learning (FL), which enables the use of a set of physically distributed data repositories, usually known as nodes, satisfying the restriction that the data do not leave the repository. Under these conditions, FL can build high-quality, accurate deep-learning models using a lot of available data wherever it is. Therefore, FL can help researchers and clinicians diagnose diseases and support medical decisions more efficiently and robustly. This article provides a systematic survey of FL in medical image analysis, specifically based on Magnetic Resonance Imaging, Computed Tomography, X-radiography, and histology images. Hence, it discusses applications, contributions, limitations, and challenges and is, therefore, suitable for those who want to understand how FL can contribute to the medical imaging domain.

Abstract
Bin picking is a challenging problem that involves using a robotic manipulator to remove, one-by-one, a set of objects randomly stacked in a container. When the objects are prone to entanglement, having an estimation of their pose and shape is highly valuable for more reliable grasp and motion planning. This paper focuses on modeling entangled tubes with varying degrees of curvature. An unconventional machine learning technique, Inductive Logic Programming (ILP), is used to construct sets of rules (theories) capable of modeling multiple tubes when given the cylinders that constitute them. Datasets of entangled tubes are created via simulation in Gazebo. Experiments using Aleph and SWI-Prolog illustrate how ILP can build explainable theories with a high performance, using a relatively small dataset and low amount of time for training. Therefore, this work serves as a proof-of-concept that ILP is a valuable method to acquire knowledge and validate heuristics for pose and shape estimation in complex bin picking scenarios.

Abstract

Abstract

Abstract
The oral microbiota is a highly complex and diversified part of the human microbiome. Being located at the interface between the human body and the exterior environment, this microbiota can deepen our understanding of the environmental impacts on the global status of human health. This research topic has been well addressed in Westernized populations, but these populations only represent a fraction of human diversity. Papua New Guinea hosts very diverse environments and one of the most unique human biological diversities worldwide. In this study we performed the first known characterization of the oral microbiome in 85 Papua New Guinean individuals living in different environments, using a qualitative and quantitative approach. We found a significant geographical structure of the Papua New Guineans oral microbiome, especially in the groups most isolated from urban spaces. In comparison to other global populations, two bacterial genera related to iron absorption were significantly more abundant in Papua New Guineans and Aboriginal Australians, which suggests a shared oral microbiome signature. Further studies will be needed to confirm and explore this possible regional-specific oral microbiome profile.