Medical Image Denoising Using Convolutional Denoising Autoencoders

By Lovedeep Gondara. 2016

641 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Media Technology

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Citations in the corpus

A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines (2018, methodology): “of the method introduced in [141], although in this case they are medical images and the AE method is founded on convolutional denosing AEs.”
Applications of Deep Learning and Reinforcement Learning to Biological Data (2017, methodology): “Gondara proposed a denoising technique utilizing convolutional denoising DA, and validated it with mammograms and dental radiography [144].”
Noise Reduction in ECG Signals Using Fully Convolutional Denoising Autoencoders (2019, methodology): “Due to their powerful nonlinear mapping capabilities, the AE and DAE models have been popularly used for data compression [20], [21] and noise reduction on speech signals [22], [23] and medical images [24].”
Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions (2017, methodology): “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].”
Secure and Robust Machine Learning for Healthcare: A Survey (2020, methodology): “models are used for denoising medical images such as convolutional denoising autoencoders [43] and GANs.”