ArticlePloS one2022
The risk of bias in denoising methods: Examples from neuroimaging.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
- Functional Imaging of Hippocampal Layers Using VASO and BOLD on the Next Generation (NexGen) 7T Scanner.Human brain mapping · 2026Article
- Functional imaging of hippocampal layers using VASO and BOLD on the Next Generation (NexGen) 7T Scanner.bioRxiv : the preprint server for biology · 2025Article
- Evaluating the impact of denoising diffusion MRI data on tractometry metrics of optic tract abnormalities in glaucoma.Scientific reports · 2025Article
- Improved quantitative parameter estimation for prostate TMagma (New York, N.Y.) · 2024Article
- Evaluating the effect of denoising submillimeter auditory fMRI data with NORDIC.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Evaluating the effect of denoising submillimeter auditory fMRI data with NORDIC.bioRxiv : the preprint server for biology · 2024Article
- Denoising diffusion MRI: Considerations and implications for analysis.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- DENOISING: Dynamic enhancement and noise overcoming in multimodal neural observations via high-density CMOS-based biosensors.Frontiers in bioengineering and biotechnology · 2024Article
- NORDIC denoising on VASO data.Frontiers in neuroscience · 2024Article
- Statistically unbiased prediction enables accurate denoising of voltage imaging data.Nature methods · 2023Article
- Rapid Synthesis of Cryo-ET Data for Training Deep Learning Models.bioRxiv : the preprint server for biology · 2023Article
- Efficient PCA denoising of spatially correlated redundant MRI data.Imaging neuroscience (Cambridge, Mass.) · 2023Article
- Serial intravital 2-photon microscopy and analysis of the kidney using upright microscopes.Frontiers in physiology · 2023Article
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Abstract
Experimental datasets are growing rapidly in size, scope, and detail, but the value of these datasets is limited by unwanted measurement noise. It is therefore tempting to apply analysis techniques that attempt to reduce noise and enhance signals of interest. In this paper, we draw attention to the possibility that denoising methods may introduce bias and lead to incorrect scientific inferences. To present our case, we first review the basic statistical concepts of bias and variance. Denoising techniques typically reduce variance observed across repeated measurements, but this can come at the expense of introducing bias to the average expected outcome. We then conduct three simple simulations that provide concrete examples of how bias may manifest in everyday situations. These simulations reveal several findings that may be surprising and counterintuitive: (i) different methods can be equally effective at reducing variance but some incur bias while others do not, (ii) identifying methods that better recover ground truth does not guarantee the absence of bias, (iii) bias can arise even if one has specific knowledge of properties of the signal of interest. We suggest that researchers should consider and possibly quantify bias before deploying denoising methods on important research data.
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