Deep Algorithm Unrolling for Biomedical Imaging

• URL: http://arxiv.org/abs/2108.06637v1
• Date: Sun, 15 Aug 2021 01:06:26 GMT
• Title: Deep Algorithm Unrolling for Biomedical Imaging
• Authors: Yuelong Li, Or Bar-Shira, Vishal Monga and Yonina C. Eldar
• Abstract summary: In this chapter, we review biomedical applications and breakthroughs via leveraging algorithm unrolling. We trace the origin of algorithm unrolling and provide a comprehensive tutorial on how to unroll iterative algorithms into deep networks. We conclude the chapter by discussing open challenges, and suggesting future research directions.
• Score: 99.73317152134028
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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