Where medical imaging meets signal processing

The Signal Processing and Biomedical Applications (SiPBA) research group is one of the leading research groups in statistical signal processing, analysis and machine learning applied to medical imaging. With an extensive background in signal processing, we are currently developing several lines of research that include neurodegenerative diseases, autism and cancer.

Statistical signal processing

Pattern recognition, structural and functional medical imaging, EEG, voice activity detection.

Machine learning

Supervised and un-supervised learning. Statistical learning and new hibrid models.

Deep learning

Novel feature extraction and detection models in large datasets with application to biomedicine.

Computer Aided Diagnosis

Classification of brain images in neurodegenerative diseases (Alzheimer's, Parkinson's), autism and breast cancer.

Our research is intended to create bridges between statistics/engineering and medicine. We develop machine learning methods to explore biomedical data, with a strong emphasis in neuroimaging. Currently, we are exploring new paths associated with time series such as EEG, functional imaging including fMRI, PET and SPECT, while maintaining our traditional lines involving structural MRI. We have succesfully applied many methodologies to evaluate the progression Alzheimer’s Disease or Parkinson’s Disease, establishing subgroups of Autism and, recently, to classify breast cancer MRI imaging.