Learning Medical Image Denoising with Deep Dynamic Residual Attention Network

9 Dec 2020  ·  S M A Sharif, Rizwan Ali Naqvi 2, Mithun Biswas ·

Image denoising performs a prominent role in medical image analysis. In many cases, it can drastically accelerate the diagnostic process by enhancing the perceptual quality of noisy image samples. However, despite the extensive practicability of medical image denoising, the existing denoising methods illustrate deficiencies in addressing the diverse range of noise appears in the multidisciplinary medical images. This study alleviates such challenging denoising task by learning residual noise from a substantial extent of data samples. Additionally, the proposed method accelerates the learning process by introducing a novel deep network, where the network architecture exploits the feature correlation known as the attention mechanism and combines it with spatially refine residual features. The experimental results illustrate that the proposed method can outperform the existing works by a substantial margin in both quantitative and qualitative comparisons. Also, the proposed method can handle real-world image noise and can improve the performance of different medical image analysis tasks without producing any visually disturbing artefacts.

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Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Medical Image Denoising Dermatologist level dermoscopy skin cancer classification using different deep learning convolutional neural networks algorithms Deep Dynamic Residual Attention Network Average PSNR 40.79 # 1
SSIM 0.9481 # 1
Medical Image Denoising Human Protein Atlas Image Deep Dynamic Residual Attention Network Average PSNR 43.84 # 1
SSIM 0.9608 # 1
Medical Image Denoising LGG Segmentation Dataset Deep Dynamic Residual Attention Network Average PSNR 40.48 # 1
SSIM 0.9511 # 1


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