Abstract
Deep neural networks have been extensively studied for denoising low-dose computed tomography (LDCT) images, but some challenges related to robustness and generalization still need to be addressed. It is known that CNN-based denoising methods perform optimally when all the training and testing images have the same noise variance, but this assumption does not hold in the case of LDCT denoising. As the variance of the CT noise varies depending on the tissue density of the scanned organ, CNNs fails to perform at their full capacity. To overcome this limitation, we propose a novel noise-conditioned feature modulation layer that scales the weight matrix values of a particular convolutional layer based on the noise level present in the input signal. This technique creates a neural network that is conditioned on the input image and can adapt to varying noise levels. Our experiments on two public benchmark datasets show that the proposed dynamic convolutional layer significantly improves the denoising performance of the baseline network, as well as its robustness and generalization to previously unseen noise levels.
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Bera, S., Biswas, P.K. (2023). Noise Conditioned Weight Modulation for Robust and Generalizable Low Dose CT Denoising. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14229. Springer, Cham. https://doi.org/10.1007/978-3-031-43999-5_9
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DOI: https://doi.org/10.1007/978-3-031-43999-5_9
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