Applications of Deep Learning in Medical Image Analysis : Grading of Prostate Cancer and Detection of Coronary Artery Disease

Abstract: A wide range of medical examinations are using analysis of images from different types of equipment. Using artificial intelligence, the assessments could be done automatically. This can have multiple benefits for the healthcare; reduce workload for medical doctors, decrease variations in diagnoses and cut waiting times for the patient as well as improve the performance. The aim of this thesis has been to develop such solutions for two common diseases: prostate cancer and coronary artery disease. The methods used are mainly based on deep learning, where the model teaches itself by training on large datasets.Prostate cancer is one of the most common cancer diagnoses among men. The diagnosis is most commonly determined by visual assessment of prostate biopsies in a light microscope according to the Gleason scale. Deep learning methods to automatically detect and grade the cancer areas are presented in this thesis. The methods have been adapted to improve the generalisation performance on images from different hospitals, images which have inevitable variations in e.g.\ stain appearance. The methods include the usage of digital stain normalisation, training with extensive augmentation or using models such as a domain-adversarial neural network. One Gleason grading algorithm was evaluated on a small cohort with biopsies annotated in detail by two pathologists, to compare the performance with pathologists' inter-observer variability. Another cancer detection algorithm was evaluated on a large active surveillance cohort, containing patients with small areas of low-grade cancer. The results are promising towards a future tool to facilitate grading of prostate cancer.Cardiovascular disease is the leading cause of death world-wide, whereof coronary artery disease is one of the most common diseases. One way to diagnose coronary artery disease is by using myocardial perfusion imaging, where disease in the three main arteries supplying the heart with blood can be detected. Methods based on deep learning to perform the detection automatically are presented in this thesis. Furthermore, an algorithm developed to predict the degree of coronary artery stenosis from myocardial perfusion imaging, by means of quantitative coronary angiography, has also been developed. This assessment is normally done using invasive coronary angiography. Making the prediction automatically from myocardial perfusion imaging could save suffering for patients and free resources within the healthcare system.