ArticleIEEE signal processing magazine2022
Unsupervised Deep Learning Methods for Biological Image Reconstruction and Enhancement: An overview from a signal processing perspective.
Article in IEEE signal processing magazine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- A single-view-based electroacoustic tomography imaging using deep learning for electroporation monitoring.Medical physics · 2026Article
- Halo-free quantitative phase imaging via physics-constrained self-supervised network.Journal of biomedical optics · 2026Article
- Lightweight CycleGAN models for cross-modality image transformation and experimental quality assessment in fluorescence microscopy.Biomedical optics express · 2026Article
- Self-supervised learning for MRI reconstruction: a review and new perspective.Magma (New York, N.Y.) · 2025Review
- Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining.ArXiv · 2025Article
- Deep Learning Assisted Outer Volume Removal for Highly-Accelerated Real-Time Dynamic MRI.ArXiv · 2025Article
- Model-Based Convolution Neural Network for 3D Near-Infrared Spectral Tomography.IEEE transactions on medical imaging · 2025Article
- In-depth and high-throughput spatial proteomics for whole-tissue slice profiling by deep learning-facilitated sparse sampling strategy.Cell discovery · 2025Article
- Computationally Efficient Implicit Training Strategy for Unrolled Networks (IMUNNE): A Preliminary Analysis Using Accelerated Real-Time Cardiac Cine MRI.IEEE transactions on bio-medical engineering · 2025Article
- Deep learning for accelerated and robust MRI reconstruction.Magma (New York, N.Y.) · 2024Review
- Unsupervised deep learning with convolutional neural networks for static parallel transmit design: A retrospective study.Magnetic resonance in medicine · 2024Article
- Zero-DeepSub: Zero-shot deep subspace reconstruction for rapid multiparametric quantitative MRI using 3D-QALAS.Magnetic resonance in medicine · 2024Article
- Missing Wedge Completion via Unsupervised Learning with Coordinate Networks.International journal of molecular sciences · 2024Article
- NON-CARTESIAN SELF-SUPERVISED PHYSICS-DRIVEN DEEP LEARNING RECONSTRUCTION FOR HIGHLY-ACCELERATED MULTI-ECHO SPIRAL FMRI.Proceedings. IEEE International Symposium on Biomedical Imaging · 2024Article
- A CONVEX COMPRESSIBILITY-INSPIRED UNSUPERVISED LOSS FUNCTION FOR PHYSICS-DRIVEN DEEP LEARNING RECONSTRUCTION.Proceedings. IEEE International Symposium on Biomedical Imaging · 2024Article
- CYCLE-CONSISTENT SELF-SUPERVISED LEARNING FOR IMPROVED HIGHLY-ACCELERATED MRI RECONSTRUCTION.Proceedings. IEEE International Symposium on Biomedical Imaging · 2024Article
- Missing Wedge Completion via Unsupervised Learning with Coordinate Networks.bioRxiv : the preprint server for biology · 2024Article
- UNCERTAINTY-GUIDED PHYSICS-DRIVEN DEEP LEARNING RECONSTRUCTION VIA CYCLIC MEASUREMENT CONSISTENCY.Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference) · 2024Article
- Opportunities and Challenges in Applying AI to Evolutionary Morphology.Integrative organismal biology (Oxford, England) · 2024Review
- SSL-QALAS: Self-Supervised Learning for rapid multiparameter estimation in quantitative MRI using 3D-QALAS.Magnetic resonance in medicine · 2023Article
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4 authors.
Funding
Abstract
Recently, deep learning approaches have become the main research frontier for biological image reconstruction and enhancement problems thanks to their high performance, along with their ultra-fast inference times. However, due to the difficulty of obtaining matched reference data for supervised learning, there has been increasing interest in unsupervised learning approaches that do not need paired reference data. In particular, self-supervised learning and generative models have been successfully used for various biological imaging applications. In this paper, we overview these approaches from a coherent perspective in the context of classical inverse problems, and discuss their applications to biological imaging, including electron, fluorescence and deconvolution microscopy, optical diffraction tomography and functional neuroimaging.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.