Deep Algorithm Unrolling for Biomedical Imaging

TL;DR

This paper reviews the use of algorithm unrolling, a technique that combines traditional iterative algorithms with deep learning, to advance biomedical imaging applications and discusses future research directions.

Contribution

It provides a comprehensive tutorial on algorithm unrolling, reviews its applications in biomedical imaging, and discusses recent trends and open challenges in the field.

Findings

Algorithm unrolling effectively bridges iterative algorithms and deep learning.

Numerous biomedical imaging modalities benefit from unrolling techniques.

Future research directions include addressing open challenges in the field.

Abstract

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.