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
This study focuses on the task of developing automated models for complex aerobatic aircraft maneuvers. The approach employed here utilizes Behavioral Cloning, a technique in which human pilots supply a series of sample maneuvers. These maneuvers serve as training data for a Machine Learning algorithm, enabling the system to generate control models for each maneuver. The optimal instances for each maneuver were chosen based on a set of objective evaluation criteria. By utilizing these selected sets of examples, resilient models were developed, capable of reproducing the maneuvers performed by the human pilots who supplied the examples. In certain instances, these models even exhibited superior performance compared to the pilots themselves, a phenomenon referred to as the clean-up effect. We also explore the application of transfer learning to adapt the developed controllers to various airplane models, revealing compelling evidence that transfer learning is effective for refining them for targeted aircraft. A comprehensive set of intricate maneuvers was executed through a meta -controller capable of orchestrating the fundamental maneuvers acquired through imitation. This undertaking yielded promising outcomes, demonstrating the proficiency of several Machine Learning models in successfully executing highly intricate aircraft maneuvers.

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
This article reports on the development of a Web platform for the study of Adverse Drug Events (ADEs). The platform is able to import ADE episodes from official Web sites, like OpenFDA, analyse the chemistry of the drugs involved, together with patient data, and produce a potential explanation based on the drugs interactions. Each study uses chemical knowledge to enrich the information on the molecules involved in the episodes. Data Mining is then used to construct models that can help in the explanation of the ADE occurrence and to predict future events. This paper reports on the Web portal developed and the Data Mining experiments conducted to evaluate the quality, and potential explanations of the forecasted adverse reactions, using real reports of drug administration and the subsequent adverse events. The results showed that it was possible to predict the outcomes of ADEs based on the structure of the molecules of the drugs involved and the data collected from real reports of drug administration up to an accuracy of 79%, while also predicting, with high accuracy, the severity of events where the outcome is the death of the patient (with a precision of 98.9%). The platform provides a less expensive and more accurate way of predicting adverse drug reactions compared to traditional methods. This study highlights the importance of understanding drug interactions at a molecular level and the usefulness of utilising Data Mining techniques in predicting ADEs.