ArticleIEEE transactions on medical imaging2023
Noise Suppression With Similarity-Based Self-Supervised Deep Learning.
Article in IEEE transactions on medical imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
24 citing papers in PubMed.
- Cross-Distribution Diffusion Priors-Driven Iterative Reconstruction for Sparse-View CT.IEEE transactions on medical imaging · 2026Article
- ZS-KAN: Zero-Shot Image Denoising With Lightweight Kolmogorov--Arnold Networks.IEEE transactions on bio-medical engineering · 2026Article
- Tomographic Foundation Model-FORCE: Flow-Oriented Reconstruction Conditioning Engine.IEEE transactions on medical imaging · 2026Article
- Deep Few-View High-Resolution Photon-Counting CT at Halved Dose for Extremity Imaging.IEEE transactions on medical imaging · 2026Article
- ZS4D: Zero-Shot Self-Similarity-Steered Denoiser for Volumetric Photon-Counting CT.IEEE transactions on radiation and plasma medical sciences · 2026Article
- Enhancing low-dose CT denoising via multi-view knowledge transfer without paired data.Frontiers in radiology · 2026Article
- Dual-Domain Self-Supervised Deep Learning with Graph Convolution for Low-Dose Computed Tomography Reconstruction.Journal of imaging informatics in medicine · 2025Article
- Sparse-view spectral CT reconstruction via a coupled subspace representation and score-based generative model.Quantitative imaging in medicine and surgery · 2025Article
- Ultra-High-Resolution Photon-Counting-Detector CT with a Dedicated Denoising Convolutional Neural Network for Enhanced Temporal Bone Imaging.AJNR. American journal of neuroradiology · 2025Article
- Unsupervised and Self-supervised Learning in Low-Dose Computed Tomography Denoising: Insights from Training Strategies.Journal of imaging informatics in medicine · 2025Review
- Patch-based dual-domain photon-counting CT data correction with residual-based WGAN-ViT.Physics in medicine and biology · 2025Article
- Reconstructing and analyzing the invariances of low-dose CT image denoising networks.Medical physics · 2025Article
- Enhancing Diagnostic Precision: A Distribution-Based Compressed Denoising Scheme using Transfer Learning for Noise Reduction in Medical Imaging.Current medical imaging · 2025Article
- Improved noise reduction in photon-counting detector CT using prior knowledge-aware iterative denoising neural network.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Learned Tensor Neural Network Texture Prior for Photon-Counting CT Reconstruction.IEEE transactions on medical imaging · 2024Article
- Self-supervised learning for CT image denoising and reconstruction: a review.Biomedical engineering letters · 2024Review
- Systematic Review on Learning-based Spectral CT.ArXiv · 2024Article
- Noise suppression in photon-counting computed tomography using unsupervised Poisson flow generative models.Visual computing for industry, biomedicine, and art · 2024Article
- UPAMNet: A unified network with deep knowledge priors for photoacoustic microscopy.Photoacoustics · 2024Article
- Contrast-enhanced photon-counting micro-CT of tumor xenograft models.Physics in medicine and biology · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Image denoising is a prerequisite for downstream tasks in many fields. Low-dose and photon-counting computed tomography (CT) denoising can optimize diagnostic performance at minimized radiation dose. Supervised deep denoising methods are popular but require paired clean or noisy samples that are often unavailable in practice. Limited by the independent noise assumption, current self-supervised denoising methods cannot process correlated noises as in CT images. Here we propose the first-of-its-kind similarity-based self-supervised deep denoising approach, referred to as Noise2Sim, that works in a nonlocal and nonlinear fashion to suppress not only independent but also correlated noises. Theoretically, Noise2Sim is asymptotically equivalent to supervised learning methods under mild conditions. Experimentally, Nosie2Sim recovers intrinsic features from noisy low-dose CT and photon-counting CT images as effectively as or even better than supervised learning methods on practical datasets visually, quantitatively and statistically. Noise2Sim is a general self-supervised denoising approach and has great potential in diverse applications.
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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.