Biography

Daniel Capellán-Martín is a Biomedical Engineer and PhD candidate at the Universidad Politécnica de Madrid (UPM), specializing in deep learning and medical imaging. His research focuses on developing AI-driven tools for medical image analysis, including tuberculosis detection in chest X-rays and brain tumor segmentation in MRI scans, among other projects. Daniel has co-authored several publications in these areas, contributing to advancements in the medical image processing field.

Expertise

TB Diagnostics
Public Health

Key Impacts

Generation of paediatric chest X-rays with TB-related findings using deep generative models

Latent diffusion models can generate coherent pediatric CXRs with TB-related findings. These synthetic images can enhance AI model performance when used for pre-training. This approach holds promise for addressing data scarcity in pediatric TB imaging and supporting the development of more accurate diagnostic tools.

Source: Conference 2024