A trusted medical image super-resolution method based on feedback adaptive weighted dense network

Low-dose CT denoising with a high-level feature refinement and dynamic convolution network

BACKGROUND

Since the potential health risks of the radiation generated by computer tomography (CT), concerns have been expressed on reducing the radiation dose. However, low-dose CT (LDCT) images contain complex noise and artifacts, bringing uncertainty to medical diagnosis.

PURPOSE

Existing deep learning (DL)-based denoising methods are difficult to fully exploit hierarchical features of different levels, limiting the effect of denoising. Moreover, the standard convolution kernel is parameter sharing and cannot be adjusted dynamically with input change. This paper proposes an LDCT denoising network using high-level feature refinement and multiscale dynamic convolution to mitigate these problems.

METHODS

The dual network structure proposed in this paper consists of the feature refinement network (FRN) and the dynamic perception network (DPN). The FDN extracts features of different levels through residual dense connections. The high-level hierarchical information is transmitted to DPN to improve the low-level representations. In DPN, the two networks' features are fused by local channel attention (LCA) to assign weights in different regions and handle CT images' delicate tissues better. Then, the dynamic dilated convolution (DDC) with multibranch and multiscale receptive fields is proposed to enhance the expression and processing ability of the denoising network. The experiments were trained and tested on the dataset "NIH-AAPM-Mayo Clinic Low-Dose CT Grand Challenge," consisting of 10 anonymous patients with normal-dose abdominal CT and LDCT at 25% dose. In addition, external validation was performed on the dataset "Low Dose CT Image and Projection Data," which included 300 chest CT images at 10% dose and 300 head CT images at 25% dose.

RESULTS

Proposed method compared with seven mainstream LDCT denoising algorithms. On the Mayo dataset, achieved peak signal-to-noise ratio (PSNR): 46.3526 dB (95% CI: 46.0121-46.6931 dB) and structural similarity (SSIM): 0.9844 (95% CI: 0.9834-0.9854). Compared with LDCT, the average increase was 3.4159 dB and 0.0239, respectively. The results are relatively optimal and statistically significant compared with other methods. In external verification, our algorithm can cope well with ultra-low-dose chest CT images at 10% dose and obtain PSNR: 28.6130 (95% CI: 28.1680-29.0580 dB) and SSIM: 0.7201 (95% CI: 0.7101-0.7301). Compared with LDCT, PSNR/SSIM is increased by 3.6536dB and 0.2132, respectively. In addition, the quality of LDCT can also be improved in head CT denoising.

CONCLUSIONS

This paper proposes a DL-based LDCT denoising algorithm, which utilizes high-level features and multiscale dynamic convolution to optimize the network's denoising effect. This method can realize speedy denoising and performs well in noise suppression and detail preservation, which can be helpful for the diagnosis of LDCT.

Densely Residual Network with Dual Attention for Hyperspectral Reconstruction from RGB Images

In the last several years, deep learning has been introduced to recover a hyperspectral image (HSI) from a single RGB image and demonstrated good performance. In particular, attention mechanisms have further strengthened discriminative features, but most of them are learned by convolutions with limited receptive fields or require much computational cost, which hinders the function of attention modules. Furthermore, the performance of these deep learning methods is hampered by tackling multi-level features equally. To this end, in this paper, based on multiple lightweight densely residual modules, we propose a densely residual network with dual attention (DRN-DA), which utilizes advanced attention and adaptive fusion strategy for more efficient feature correlation learning and more powerful feature extraction. Specifically, an SE layer is applied to learn channel-wise dependencies, and dual downsampling spatial attention (DDSA) is developed to capture long-range spatial contextual information. All the intermediate-layer feature maps are adaptively fused. Experimental results on four data sets from the NTIRE 2018 and NTIRE 2020 Spectral Reconstruction Challenges demonstrate the superiority of the proposed DRN-DA over state-of-the-art methods (at least −6.19% and −1.43% on NTIRE 2018 “Clean” and “Real World” track, −6.85% and −5.30% on NTIRE 2020 “Clean” and “Real World” track) in terms of mean relative absolute error.

Dual U-Net residual networks for cardiac magnetic resonance images super-resolution

BACKGROUND AND OBJECTIVE

Heart disease is a vital disease that has threatened human health, and is the number one killer of human life. Moreover, with the added influence of recent health factors, its incidence rate keeps showing an upward trend. Today, cardiac magnetic resonance (CMR) imaging can provide a full range of structural and functional information for the heart, and has become an important tool for the diagnosis and treatment of heart disease. Therefore, improving the image resolution of CMR has an important medical value for the diagnosis and condition assessment of heart disease. At present, most single-image super-resolution (SISR) reconstruction methods have some serious problems, such as insufficient feature information mining, difficulty to determine the dependence of each channel of feature map, and reconstruction error when reconstructing high-resolution image.

METHODS

To solve these problems, we have proposed and implemented a dual U-Net residual network (DURN) for super-resolution of CMR images. Specifically, we first propose a U-Net residual network (URN) model, which is divided into the up-branch and the down-branch. The up-branch is composed of residual blocks and up-blocks to extract and upsample deep features; the down-branch is composed of residual blocks and down-blocks to extract and downsample deep features. Based on the URN model, we employ this a dual U-Net residual network (DURN) model, which combines the extracted deep features of the same position between the first URN and the second URN through residual connection. It can make full use of the features extracted by the first URN to extract deeper features of low-resolution images.

RESULTS

When the scale factors are 2, 3, and 4, our DURN can obtain 37.86 dB, 33.96 dB, and 31.65 dB on the Set5 dataset, which shows (i) a maximum improvement of 4.17 dB, 3.55 dB, and 3.22dB over the Bicubic algorithm, and (ii) a minimum improvement of 0.34 dB, 0.14 dB, and 0.11 dB over the LapSRN algorithm.

CONCLUSION

Comprehensive experimental study results on benchmark datasets demonstrate that our proposed DURN can not only achieve better performance for peak signal to noise ratio (PSNR) and structural similarity index (SSIM) values than other state-of-the-art SR image algorithms, but also reconstruct clearer super-resolution CMR images which have richer details, edges, and texture.

Multi-disease big data analysis using beetle swarm optimization and an adaptive neuro-fuzzy inference system

Healthcare organizations and Health Monitoring Systems generate large volumes of complex data, which offer the opportunity for innovative investigations in medical decision making. In this paper, we propose a beetle swarm optimization and adaptive neuro-fuzzy inference system (BSO-ANFIS) model for heart disease and multi-disease diagnosis. The main components of our analytics pipeline are the modified crow search algorithm, used for feature extraction, and an ANFIS classification model whose parameters are optimized by means of a BSO algorithm. The accuracy achieved in heart disease detection is $$99.1\%$$ 99.1 % with $$99.37\%$$ 99.37 % precision. In multi-disease classification, the accuracy achieved is $$96.08\%$$ 96.08 % with $$98.63\%$$ 98.63 % precision. The results from both tasks prove the comparative advantage of the proposed BSO-ANFIS algorithm over the competitor models.