mlsanityData to 7 Oct 2026

Deep Algorithm Unrolling for Biomedical Imaging

arXiv 2108.06637 ↗First posted 15 Aug 2021cs.CVcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 23 of its 47 references that are in the map. First posted on arXiv 15 Aug 2021.

Builds on

23 of 47 references.

References · 23

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2016Deep Convolutional Neural Network for Inverse Problems in Imagingcited by 431
  3. 2019Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processingcited by 411
  4. 2017MoDL: Model Based Deep Learning Architecture for Inverse Problemscited by 384
  5. 2017Learned Primal-dual Reconstructioncited by 251
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  7. 2018Theoretical Linear Convergence of Unfolded ISTA and its Practical Weights and Thresholdscited by 83
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  9. 2019Plug and play methods for magnetic resonance imaging (long version)cited by 75
  10. 2015Neural Network with Unbounded Activation Functions is Universal Approximatorcited by 63
  11. 2016Deep Learning in Bioinformaticscited by 48
  12. 2017Model based learning for accelerated, limited-view 3D photoacoustic tomographycited by 45
11 more
  1. 2018Deep Unfolded Robust PCA with Application to Clutter Suppression in Ultrasoundcited by 43
  2. 2019Deep learning in ultrasound imagingcited by 38
  3. 2016Maximal Sparsity with Deep Networks?cited by 29
  4. 2019Deep Generalization of Structured Low-Rank Algorithms (Deep-SLR)cited by 29
  5. 2018Deep BCD-Net Using Identical Encoding-Decoding CNN Structures for Iterative Image Recoverycited by 11
  6. 2020Learned SPARCOM: Unfolded Deep Super-Resolution Microscopycited by 11
  7. 2019BCD-Net for Low-dose CT Reconstruction: Acceleration, Convergence, and Generalizationcited by 7
  8. 2021Learned super resolution ultrasound for improved breast lesion characterizationcited by 7
  9. 2017SPARCOM: Sparsity Based Super-Resolution Correlation Microscopycited by 6
  10. 2016A Fast Algorithm for Convolutional Structured Low-Rank Matrix Recoverycited by 5
  11. 2017End-to-end Lung Nodule Detection in Computed Tomographycited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 0; 2023: 2; 2024: 0; 2025: 1; 2026: 2.

6 papers.

  1. 2023Robust Stochastically-Descending Unrolled Networkscited by 9
  2. 2021Unrolled Variational Bayesian Algorithm for Image Blind Deconvolutioncited by 4
  3. 2023A Deep Unrolling Model with Hybrid Optimization Structure for Hyperspectral Image Deconvolutioncited by 1
  4. 2025Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problemscited by 1
  5. 2026Unrolled Neural Networks for Constrained Optimizationcited by 0
  6. 2026Matrix-Free Photoacoustic Image Reconstruction via Sensor-Token Self-Attentioncited by 0