Image processing focuses on data science research in biomedical image and signal processing, artificial intelligence, and machine learning to support automated clinical decision-making in disease screening and diagnostics. This area of research includes image and text analysis for clinical research, exploration of visual content relevant to disease in images and video, and visual information retrieval for embedding automated decision-support systems in diagnostic and treatment pathways.
Research in machine learning and artificial intelligence (ML/AI) algorithms aims to improve computer-aided disease detection, accuracy and reliability. We develop novel computational solutions to analyze chest x-rays (CXR) and screen for cardiopulmonary diseases with a special interest in pulmonary TB in HIV+ population.
The goal of our work in Biomedical Imaging is two-fold: One, to develop advanced imaging tools for biomedical research in partnership with the National Cancer Institute and other organizations. Secondly, to conduct research in Content Based Image Retrieval (CBIR) to index and retrieve medical images by image features (e.g., shape, color and texture), augmented by textual features as well.
To improve malaria diagnostics, we are developing a fully-automated system for parasite detection and counting in blood films in collaboration with NIH’s National Institute of Allergy and Infectious Diseases (NIAID) and Mahidol-Oxford University.