Obtaining MRI data in medicine is challenging due to privacy concerns, such as patient confidentiality, and logistical issues like limited access to specialized imaging equipment or variability in data acquisition protocols. On the other hand, deep learning models thrive on large datasets, and their performance often suffers in data-scarce...

Facial Expression Recognition (FER) is crucial in social communication and in various applied fields, such as social robots, healthcare, and driver fatigue surveillance. Central to FER are Action Units (AUs) and the Facial Action Coding System (FACS), which provide a systematic approach to categorize facial movements. However, AUs are unable...

Integrating 3D magnetic resonance imaging (MRI) with machine learning has shown promising results in healthcare, especially in detecting Alzheimer's Disease (AD). However, changes in MRI technologies and acquisition protocols often yield limited data, leading to potential overfitting. From the machine learning perspective, data acquired with 1.5T...

The goal of the research is to investigate the structural 3D brain T1-weighted MRI as tool to infer information about Amyotrophic Lateral Sclerosis (ALS). The project poses a challenge because T1-weighted MRI has traditionally been overlooked by clinicians, as it does not provide visual information that can be easily perceived by the human eye.