UC Berkeley

Medical image is an important information source to understand the patient’s condition. As a result, interpreting the medical images is a critical part of the clinical procedure. However, physicians’ visual evaluation of medical images in clinics has a few challenges such as the limited human resources, the unavailability of appropriate experts, the increasing number ofmedical images, and so on.

Deep learning is a subarea of machine learning that uses deep neural networks to learn the patterns behind the given data set. Deep learning showed excellent performances in image analysis problems including medical image analysis. In this dissertation, I propose and evaluate new methods for evaluating the images of three different vision problems inophthalmology.

The first problem is the early detection of retinopathy of prematurity (ROP). ROP is a leading cause of childhood blindness globally, and early detection is a key to preventing ROP to progress to severe conditions. We developed two convolutional neural networks with different depths. The deeper model showed an excellent performance including bettermetrics than an experienced human expert.

The second problem is about transfer learning in retinal vascular diseases. We propose a transfer learning method that uses the detection of a well-studied retinal vascular disease as a source problem and uses the knowledge to the detection of an under-studied retinal vascular disease. Our proposed method showed better performance with more robustness tothe stochasticity in the training process and the reduction of sample size.

The final problem is to predict the treatment response to a drug from the baseline characteristics. Both symbolic features like clinical measurements and medical images are considered for the modeling. To merge the two types of input, we proposed two approaches. The results showed the potential of the proposed method to the problem.

Deep learning is successfully applied to three medical image analysis problems in ophthalmology in this dissertation. These results offer key evidence for further development of deep learning-based medical image analysis systems in the future.