Deep Algorithm Unrolling for Biomedical Imaging
It provides a tutorial and overview for researchers in biomedical imaging, but is incremental as it reviews existing techniques rather than presenting new results.
This chapter reviews the application of algorithm unrolling, a technique that connects traditional iterative algorithms with deep learning, to biomedical imaging, highlighting its effectiveness and covering various modalities and recent works.
In this chapter, we review biomedical applications and breakthroughs via leveraging algorithm unrolling, an important technique that bridges between traditional iterative algorithms and modern deep learning techniques. To provide context, we start by tracing the origin of algorithm unrolling and providing a comprehensive tutorial on how to unroll iterative algorithms into deep networks. We then extensively cover algorithm unrolling in a wide variety of biomedical imaging modalities and delve into several representative recent works in detail. Indeed, there is a rich history of iterative algorithms for biomedical image synthesis, which makes the field ripe for unrolling techniques. In addition, we put algorithm unrolling into a broad perspective, in order to understand why it is particularly effective and discuss recent trends. Finally, we conclude the chapter by discussing open challenges, and suggesting future research directions.