Medical Image Denoising Using Convolutional Denoising Autoencoders
Open accessAbstract
Image denoising is an important pre-processing step in medical image analysis. Different algorithms have been proposed in past three decades with varying denoising performances. More recently, having outperformed all conventional methods, deep learning based models have shown a great promise. These methods are however limited for requirement of large training sample size and high computational costs. In this paper we show that using small sample size, denoising autoencoders constructed using convolutional layers can be used for efficient denoising of medical images. Heterogeneous images can be combined to boost sample size for increased denoising performance. Simplest of networks can reconstruct images with corruption levels so high that noise and signal are not differentiable to human eye.
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New methods for preprocessing routines are arising, including deep learning based registration, skull stripping, and noise reduction.
“At the same time, the new methods for these preprocessing routines are also arising, including deep learning based registration [23], skull stripping [24], and noise reduction [25].”
From Deep learning for brain MRI segmentation: state of the art and future directions (2017) · cites this paper as [25]
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- Authors
- Gondara, L.
- Affiliations
- Simon Fraser University (CA)
- Corresponding author
- Gondara, Lovedeep
- ORCID
- Gondara, Lovedeep
- Venue
- 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW)
- Publisher
- IEEE
- Published
- 2016-12
- Pages
- 241-246
- ISSN
- 2375-9259 (ISSN-L)
- OpenAlex
- W2510850936
- Cited by
- 737
- References
- 32
- Citations by year
- 2026: 24 · 2025: 70 · 2024: 122 · 2023: 125 · 2022: 115 · 2021: 118 · 2020: 78 · 2019: 49 · 2018: 24 · 2017: 11
- FWCI
- 9.72
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE ... International Conference on Data Mining workshops (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Enhancement Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
- Keywords
- medical image denoising, convolutional denoising autoencoder, small training sample size, deep learning denoising, image reconstruction, noise reduction
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