ArticleIEEE transactions on medical imaging2020
Quadratic Autoencoder (Q-AE) for Low-Dose CT Denoising.
Article in IEEE transactions on medical imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 citing papers in PubMed.
- Low-dose CT imaging using a regularization-enhanced efficient diffusion probabilistic model.Medical physics · 2026Article
- Evaluating the impact of deep learning-based image denoising on low-dose CT for lung cancer screening.Journal of applied clinical medical physics · 2026Article
- A new hybrid model for enhancing low-dose CT images using EfficientNetV2 and WGAN-GP: a multi-loss approach.European journal of medical research · 2025Article
- Dual-Domain Self-Supervised Deep Learning with Graph Convolution for Low-Dose Computed Tomography Reconstruction.Journal of imaging informatics in medicine · 2025Article
- Low-Dose CT Imaging Using a Regularization-Enhanced Efficient Diffusion Probabilistic Model.ArXiv · 2025Article
- Noise-augmented deep denoising: A method to boost CT image denoising networks.Medical physics · 2025Article
- Supervision by Denoising.IEEE transactions on pattern analysis and machine intelligence · 2025Article
- Wavelet-domain frequency-mixing transformer unfolding network for low-dose computed tomography image denoising.Quantitative imaging in medicine and surgery · 2025Article
- Low-dose computed tomography image denoising using pixel level non-local self-similarity prior with non-local means for healthcare informatics.Scientific reports · 2025Article
- Brain tumor intelligent diagnosis based on Auto-Encoder and U-Net feature extraction.PloS one · 2025Article
- Benchmarking deep learning-based low-dose CT image denoising algorithms.Medical physics · 2024Article
- Self-supervised learning for chest computed tomography: training strategies and effect on downstream applications.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- A systematic review of deep learning-based denoising for low-dose computed tomography from a perceptual quality perspective.Biomedical engineering letters · 2024Review
- LoMAE: Simple Streamlined Low-Level Masked Autoencoders for Robust, Generalized, and Interpretable Low-Dose CT Denoising.IEEE journal of biomedical and health informatics · 2024Article
- Systematic Review on Learning-based Spectral CT.ArXiv · 2024Article
- Systematic Review on Learning-based Spectral CT.IEEE transactions on radiation and plasma medical sciences · 2024Article
- CT image denoising methods for image quality improvement and radiation dose reduction.Journal of applied clinical medical physics · 2024Review
- Multi-Scale Feature Fusion Network for Low-Dose CT Denoising.Journal of digital imaging · 2023Article
- [Opportunities for artificial intelligence in radiation protection : Improving safety of diagnostic imaging].Radiologie (Heidelberg, Germany) · 2023Review
- A review on Deep Learning approaches for low-dose Computed Tomography restoration.Complex & intelligent systems · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Inspired by complexity and diversity of biological neurons, our group proposed quadratic neurons by replacing the inner product in current artificial neurons with a quadratic operation on input data, thereby enhancing the capability of an individual neuron. Along this direction, we are motivated to evaluate the power of quadratic neurons in popular network architectures, simulating human-like learning in the form of "quadratic-neuron-based deep learning". Our prior theoretical studies have shown important merits of quadratic neurons and networks in representation, efficiency, and interpretability. In this paper, we use quadratic neurons to construct an encoder-decoder structure, referred as the quadratic autoencoder, and apply it to low-dose CT denoising. The experimental results on the Mayo low-dose CT dataset demonstrate the utility and robustness of quadratic autoencoder in terms of image denoising and model efficiency. To our best knowledge, this is the first time that the deep learning approach is implemented with a new type of neurons and demonstrates a significant potential in the medical imaging field.
Indexed as
Identifiers
What OpenQuestion holds
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.