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Publicações

2025

FedGS: Federated Gradient Scaling for Heterogeneous Medical Image Segmentation

Autores
Schutte, P; Corbetta, V; Beets-Tan, R; Silva, W;

Publicação
Lecture Notes in Computer Science - Medical Image Computing and Computer Assisted Intervention – MICCAI 2024 Workshops

Abstract

2025

Clinical Annotation and Medical Image Anonymization for AI Model Training in Lung Cancer Detection

Autores
Freire, AM; Rodrigues, EM; Sousa, JV; Gouveia, M; Ferreira-Santos, D; Pereira, T; Oliveira, HP; Sousa, P; Silva, AC; Fernandes, MS; Hespanhol, V; Araújo, J;

Publicação
UNIVERSAL ACCESS IN HUMAN-COMPUTER INTERACTION, UAHCI 2025, PT I

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.

2025

Business Models for Energy Community with Vulnerable Consumers

Autores
Santos, T; Silva, R; Mello, J; Villar, J;

Publicação
2025 21ST INTERNATIONAL CONFERENCE ON THE EUROPEAN ENERGY MARKET, EEM

Abstract
Renewable energy communities (REC) can involve final consumers into the energy system incentivizing investments in decentralized renewable energy sources and shaping their energy behaviour to improve the local balance of consumption and generation. However, RECs can also help alleviate energy poverty, which occurs when low incomes and inefficient buildings and appliances result in disproportionately high energy costs for households, by lowering energy expenses through the sharing of surplus electricity at reduced prices with vulnerable members. This work explores REC business models with the specific focus on incorporating and empowering vulnerable consumers. Based on the literature review, we propose indexes to assess the vulnerability and non-vulnerability of REC members. From these indexes, we propose two business models based on two different strategies for the operation and settlement of a REC with flexible assets and vulnerable members.

2025

Tempo: ML-KEM to PAKE Compiler Resilient to Timing Attacks

Autores
Arriaga, A; Barbosa, M; Jarecki, S;

Publicação
IACR Cryptol. ePrint Arch.

Abstract

2025

Mast: interpretable stress testing via meta-learning for forecasting model robustness evaluation

Autores
Inácio, R; Cerqueira, V; Barandas, M; Soares, C;

Publicação
Mach. Learn.

Abstract

2025

Histopoly: A serious game for teaching histology to 1st year veterinary students

Autores
Marcos, R; Gomes, A; Santos, M; Coelho, A;

Publicação
ANATOMICAL SCIENCES EDUCATION

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.

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