Deep learning for medical imaging in developing nations

Deep learning research and innovation have primarily been focused on high- income countries with abundant imaging data, IT infrastructures, local equipment, and clinical expertise. While the application of Deep Learning (DL) in medical imag- ing has gained popularity, particularly for its ability to perform on par with medical experts and bring new promises to the field of medicine, progress in limited-resource environments where medical imaging is crucial has been relatively slow. For instance, in Sub-Saharan Africa, the rate of perinatal mortality, which refers to baby deaths during pregnancy or the first week due to healthcare/maternal issues, is very high due to limited access to antenatal screening. In these countries, deep learning models could be implemented to help clinicians acquire fetal ultrasound planes for the diagnosis of fetal abnormalities. Although the latest deep learning models have been able to identify standard fetal planes, there is no evidence of their ability to generalize in settings with limited resources, such as areas with restricted access to high-end ultrasound equipment and ultrasound data, or different populations. How can breakthroughs in medical deep learning research be disseminated to the global community? Moreover, how can individuals outside of America benefit from and leverage its value? In order for deep learning models to be adopted in developing countries, there is a need for greater efficiency, and we also require more robust and privacy-preserving models to make them practical. With these questions in mind, my thesis centers on the use of deep learning in healthcare for developing nations. Specifically, I explore and propose efficient, privacy-preserving, and robust machine learning techniques to enhance the efficacy of deep learning models in healthcare. Additionally, I conduct a review of the current state of healthcare in developing regions around the world and consider how deep learning can be utilized to improve patient outcomes and support clinicians.