Deep Learning

X-ray in-depth decomposition: Revealing the latent structures

X-ray is the most readily available imaging modality and has a broad range of applications that spans from diagnosis to intra-operative guidance in cardiac, orthopedics, and trauma procedures. Proper interpretation of the hidden and obscured anatomy …

X-Ray PoseNet: 6 DoF pose estimation for mobile X-Ray devices

Precise reconstruction of 3D volumes from X-ray projections requires precisely pre-calibrated systems where accurate knowledge of the systems geometric parameters is known ahead. However, when dealing with mobile X-ray devices such calibration …

Cathnets: detection and single-view depth prediction of catheter electrodes

AI Week: Synergizing Human and AI for Multidisciplinary Solutions (SHAMS)

This 5-day intensive course is tailored for PhD students from diverse disciplines seeking a robust introduction to Artificial Intelligence (AI) and Machine Learning (ML). The module begins with essential mathematical concepts, including algebra, probability, and optimization, before moving into fundamental and advanced machine learning techniques. Students will explore deep learning architectures, engage in hands-on coding exercises, and apply their knowledge to real-world problems, with a particular focus on healthcare and resource-constrained environments. The course also addresses the ethical implications of AI and the importance of explainability in AI models, preparing students to implement AI solutions responsibly in their research.

Course: AI for Medical Diagnosis and Prediction (AAI643O)

This 8-week intensive course provides non-computer science students with the essential skills to apply AI in medical diagnosis and prediction. The course covers topics such as medical image classification, detection, segmentation, and reconstruction, as well as time-series classification, regression, forecasting, and learning techniques like weakly-, semi-, and self-supervised learning. Ethical considerations, fairness, and robustness in AI are also highlighted.

MA Thesis: Deep Learning-based method for virtual ECV in cardiac magnetic resonance imaging

Abstract. Diseases of the cardiovascular system are among the most common diseases worldwide and are the leading cause of death. The World Health Organization (WHO) estimates that about 17.9 million people die of cardiovascular diseases each year worldwide.