Abstract

Abstract
The paper presents a new control concept based on the process moment instead of the process states or the process output signal. The control scheme is based on separate control of reference tracking and disturbance rejection. The tracking control is achieved by additionally feeding the input of the process model by the scaled output signal of the process model. The advantage of such feedback is that the final state of the process output can be analytically calculated and used for control instead of the actual process output value. The disturbance rejection, including model imperfections, is controlled by feeding back the filtered difference between the process output and the model output to the process input. The performance of tracking and disturbance rejection is simply controlled by two user-defined gains. Several examples have shown that the new control method provides very good and stable tracking and disturbance rejection performance.

Abstract

Abstract
Lung cancer remains one of the most common and lethal forms of cancer, with approximately 1.8 million deaths annually, often diagnosed at advanced stages. Early detection is crucial, but it depends on physicians’ accurate interpretation of computed tomography (CT) scans, a process susceptible to human limitations and variability. ByMe has developed a medical image annotation and anonymization tool designed to address these challenges through a human-centered approach. The tool enables physicians to seamlessly add structured attribute-based annotations (e.g., size, location, morphology) directly within their established workflows, ensuring intuitive interaction.Integrated with Picture Archiving and Communication Systems (PACS), the tool streamlines the annotation process and enhances usability by offering a dedicated worklist for retrospective and prospective case analysis. Robust anonymization features ensure compliance with privacy regulations such as the General Data Protection Regulation (GDPR), enabling secure dataset sharing for research and developing artificial intelligence (AI) models. Designed to empower AI integration, the tool not only facilitates the creation of high-quality datasets but also lays the foundation for incorporating AI-driven insights directly into clinical workflows. Focusing on usability, workflow integration, and privacy, this innovation bridges the gap between precision medicine and advanced technology. By providing the means to develop and train AI models for lung cancer detection, it holds the potential to significantly accelerate diagnosis as well as enhance its accuracy and consistency.

Abstract
Histology is a preclinical subject transversal in medical, dental, and veterinary curricula. Classical teaching approaches in histology are often undermined by lower motivation and engagement of students, which may be addressed by innovative learning environments. Herein, we developed a serious game approach and compared it with a classical teaching style. The students' feedback was evaluated by questionnaires, and their performance on quizzes and exam's scores were assessed. The serious game (Histopoly) consisted of a game-based web application for the teacher/game master, a digital gaming application used by the students as a controller, and a projected digital board game. The board featured rows for the four fundamental tissues (epithelial, connective, muscular, and nervous) paired with question tiles and additional tiles with more demanding activities (e.g., drawing, presenting slides, and making a syllabus). Participants included all veterinary students enrolled in the first year. Paired laboratory sessions were split with four sections (n = 94 students) playing Histopoly at the end of all sessions and two sections (n = 28 students) completing small evaluations every three weeks at the beginning of sessions. According to the questionnaires, students that played the serious game were more motivated, engaged, and more interconnected with classmates. The activity was considered fun, and students enjoyed the classes more. No differences in the final examination scores were found, but the percentage of correct answers provided throughout the serious game was significantly higher. Overall, these findings argue for the inclusion of serious games in modern histology teaching to promote student engagement in learning.

Abstract
Prognosis after intracranial hemorrhage (ICH) is influenced by a complex interplay between imaging and tabular data. Rapid and reliable prognosis are crucial for effective patient stratification and informed treatment decision-making. In this study, we aim to enhance image-based prognosis by learning a robust feature representation shared between prognosis and the clinical and demographic variables most highly correlated with it. Our approach mimics clinical decision-making by reinforcing the model to learn valuable prognostic data embedded in the image. We propose a 3D multi-task image model to predict prognosis, Glasgow Coma Scale and age, improving accuracy and interpretability. Our method outperforms current state-of-the-art baseline image models, and demonstrates superior performance in ICH prognosis compared to four board-certified neuroradiologists using only CT scans as input. We further validate our model with interpretability saliency maps. Code is available at https://github.com/MiriamCobo/MultitaskLearning_ICH_Prognosis.git.