Twinned Residual Auto-Encoder (TRAE)—A new DL architecture for denoising super-resolution and task-aware feature learning from COVID-19 CT images

Baccarelli, Enzo; Scarpiniti, Michele; Momenzadeh, Alireza

doi:10.1016/j.eswa.2023.120104

Expert Systems with Applications

2023

DOI: 10.1016/j.eswa.2023.120104

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Twinned Residual Auto-Encoder (TRAE)—A new DL architecture for denoising super-resolution and task-aware feature learning from COVID-19 CT images

Enzo Baccarelli¹,

Michele Scarpiniti²,

Alireza Momenzadeh³

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2024

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Cited by 3 publications

References 42 publications

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Multi-resolution Twinned Residual Auto-Encoders (MR-TRAE)—A Novel DL Model for Image Multi-resolution

Momenzadeh,

Baccarelli,

Scarpiniti

et al. 2024

Cogn Comput

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In this paper, we design and evaluate the performance of the Multi-resolution Twinned Residual Auto-Encoders (MR-TRAE) model, a deep learning (DL)-based architecture specifically designed for achieving multi-resolution super-resolved images from low-resolution (LR) inputs at various scaling factors. For this purpose, we expand on the recently introduced Twinned Residual Auto-Encoders (TRAE) paradigm for single-image super-resolution (SISR) to extend it to the multi-resolution (MR) domain. The main contributions of this work include (i) the architecture of the MR-TRAE model, which utilizes cascaded trainable up-sampling modules for progressively increasing the spatial resolution of low-resolution (LR) input images at multiple scaling factors; (ii) a novel loss function designed for the joint and semi-blind training of all MR-TRAE model components; and (iii) a comprehensive analysis of the MR-TRAE trade-off between model complexity and performance. Furthermore, we thoroughly explore the connections between the MR-TRAE architecture and broader cognitive paradigms, including knowledge distillation, the teacher-student learning model, and hierarchical cognition. Performance evaluations of the MR-TRAE benchmarked against state-of-the-art models (such as U-Net, generative adversarial network (GAN)-based, and single-resolution baselines) were conducted using publicly available datasets. These datasets consist of LR computer tomography (CT) scans from patients with COVID-19. Our tests, which explored multi-resolutions at scaling factors $$\times (2,4,8)$$ × ( 2 , 4 , 8 ) , showed a significant finding: the MR-TRAE model can reduce training times by up to $$60\%$$ 60 % compared to those of the baselines, without a noticeable impact on achieved performance.

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Multi-resolution Twinned Residual Auto-Encoders (MR-TRAE)—A Novel DL Model for Image Multi-resolution

Momenzadeh,

Baccarelli,

Scarpiniti

et al. 2024

Cogn Comput

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Recent developments in denoising medical images using deep learning: An overview of models, techniques, and challenges

Nazir,

Sarwar,

Saini

2024

Micron

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Global and local feature extraction based on convolutional neural network residual learning for MR image denoising

Li,

Yun,

Liu

et al. 2024

Phys. Med. Biol.

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Objective. Given the different noise distribution information of global and local magnetic resonance (MR) images, this study aims to extend the current work on convolutional neural networks that preserve global structure and local details in MR image denoising tasks. Approach. This study proposed a parallel and serial network for denoising 3D MR images, called 3D-PSNet. We use the residual depthwise separable convolution block to learn the local information of the feature map, reduce the network parameters, and thus improve the training speed and parameter efficiency. In addition, we consider the feature extraction of the global image and utilize residual dilated convolution to process the feature map to expand the receptive field of the network and avoid the loss of global information. Finally, we combine both of them to form a parallel network. What’s more, we integrate reinforced residual convolution blocks with dense connections to form serial network branches, which can remove redundant information and refine features to further obtain accurate noise information. The peak signal-to-noise ratio, structural similarity index measure, and root mean square error metrics of 3D-PSNet are as high as 47.79%, 99.81%, and 0.40%, respectively, achieving competitive denoising effect on three public datasets. The ablation experiments demonstrated the effectiveness of all the designed modules regarding all the evaluated metrics in both datasets. Significance. The proposed 3D-PSNet takes advantage of multi-scale receptive fields, local feature extraction and residual dense connections to more effectively restore the global structure and local fine features in MR images, and is expected to help doctors quickly and accurately diagnose patients' conditions.

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Twinned Residual Auto-Encoder (TRAE)—A new DL architecture for denoising super-resolution and task-aware feature learning from COVID-19 CT images

Cited by 3 publications

References 42 publications

Multi-resolution Twinned Residual Auto-Encoders (MR-TRAE)—A Novel DL Model for Image Multi-resolution

Multi-resolution Twinned Residual Auto-Encoders (MR-TRAE)—A Novel DL Model for Image Multi-resolution

Recent developments in denoising medical images using deep learning: An overview of models, techniques, and challenges

Global and local feature extraction based on convolutional neural network residual learning for MR image denoising

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