A review on Deep Learning approaches for low-dose Computed Tomography restoration

Kulathilake, K.A.S.H.; Abdullah, Nor Aniza; Sabri, Aznul Qalid Md; Lai, Khin Wee

doi:10.1007/s40747-021-00405-x

Cited by 57 publications

(48 citation statements)

References 84 publications

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“…The adoption of deep learning approaches has skyrocketed in the past two decades from various research disciplines [98,99]. The sheer benefits of using deep learning approaches in recommender system has been impressive.…”

Section: Discussionmentioning

confidence: 99%

Eliciting Auxiliary Information for Cold Start User Recommendation: A Survey

et al. 2021

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Recommender systems suggest items of interest to users based on their preferences. These preferences are typically generated from user ratings of the items. If there are no ratings for a certain user or item, it is said that there is a cold start problem, which leads to unreliable recommendations. Existing studies that reviewed and examined cold start in recommender systems have not explained the process of deriving and obtaining the auxiliary information needed for cold start recommendation. This study surveys the existing literature in order to explain the various approaches and techniques employed by researchers and the challenges associated with deriving and obtaining the auxiliary information necessary for cold start recommendation. Results show that auxiliary information for cold start recommendation is obtained by adapting traditional filtering and matrix factorization algorithms typically with machine learning algorithms to build learning prediction models. The understanding of similar or connected user profiles can be used as auxiliary information for building cold start user profile to enable similar recommendations in social networks. Similar users are clustered into sub-groups so that a cold start user could be allocated and inferred to a sub-group having similar profiles for recommendations. The key challenges of the process for obtaining the auxiliary information involve: (1) two separate recommendation processes of conversion from pure cold start to warm start before eliciting the auxiliary information; (2) the obtained implicit auxiliary information is usually ranked and sieved in order to select the top rated and reliable auxiliary information for the recommendation. This study also found that cold start user recommendation has frequently been researched in the entertainment domain, typically using music and movie data, while little research has been carried out in educational institutions and academia, or with cold start for mobile applications.

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Section: Discussionmentioning

confidence: 99%

Eliciting Auxiliary Information for Cold Start User Recommendation: A Survey

et al. 2021

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“…The criteria are numerical and used for clustering processes in the first stage. The appropriate combination of features may assist the machine learning application in finding the requirement of the descriptions for generating the desired output ( 46 ). The novel features and criteria of the medical equipment sample are tabulated in Table 3 .…”

Section: Methodsmentioning

confidence: 99%

Prioritisation Assessment and Robust Predictive System for Medical Equipment: A Comprehensive Strategic Maintenance Management

Zamzam

Al-Ani

Wahab

et al. 2021

Front. Public Health

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The advancement of technology in medical equipment has significantly improved healthcare services. However, failures in upkeeping reliability, availability, and safety affect the healthcare services quality and significant impact can be observed in operations' expenses. The effective and comprehensive medical equipment assessment and monitoring throughout the maintenance phase of the asset life cycle can enhance the equipment reliability, availability, and safety. The study aims to develop the prioritisation assessment and predictive systems that measure the priority of medical equipment's preventive maintenance, corrective maintenance, and replacement programmes. The proposed predictive model is constructed by analysing features of 13,352 medical equipment used in public healthcare clinics in Malaysia. The proposed system comprises three stages: prioritisation analysis, model training, and predictive model development. In this study, we proposed 16 combinations of novel features to be used for prioritisation assessment and prediction of preventive maintenance, corrective maintenance, and replacement programme. The modified k-Means algorithm is proposed during the prioritisation analysis to automatically distinguish raw data into three main clusters of prioritisation assessment. Subsequently, these clusters are fed into and tested with six machine learning algorithms for the predictive prioritisation system. The best predictive models for medical equipment's preventive maintenance, corrective maintenance, and replacement programmes are selected among the tested machine learning algorithms. Findings indicate that the Support Vector Machine performs the best in preventive maintenance and replacement programme prioritisation predictive systems with the highest accuracy of 99.42 and 99.80%, respectively. Meanwhile, K-Nearest Neighbour yielded the highest accuracy in corrective maintenance prioritisation predictive systems with 98.93%. Based on the promising results, clinical engineers and healthcare providers can widely adopt the proposed prioritisation assessment and predictive systems in managing expenses, reporting, scheduling, materials, and workforce.

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“…Generally, radiation reduction is usually performed by controlling the X-ray current tube or by minimizing the X-ray photon count (Kulathilake et al, 2021). This process degrades the signal-tonoise ratio (SNR) of the X-ray signals, resulting in lower-quality CT images with noise artifacts, making clinical diagnosis less reliable.…”

Section: Introductionmentioning

confidence: 99%

“…Since BAFGAN has a complex architecture and faced instability issues, the proposed denoiser utilizes dilated convolutional layers and skip connections for faster network training, better stability, and more effective fusion of the feature attention modules. In this experiment, FAM-DRL would be optimized using the combination of perceptual loss via VGG-16 Net for the prevention of edge oversmoothing, structural dissimilarity loss (DSSIM) for texture enhancement, and per-pixel loss for the symmetry between NDCT and LDCT images (Kulathilake et al, 2021). The main contribution of this paper is the unique architecture of the proposed denoising network which achieves the following: 1) protection of edges from blurring, 2) enhancement of image textures, and 3) preservation of structural details of the CT images.…”

Section: Introductionmentioning

confidence: 99%

Low Dose CT Denoising by ResNet With Fused Attention Modules and Integrated Loss Functions

Marcos¹,

Alirezaie²,

Babyn³

2022

Front. Signal Process.

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X-ray computed tomography (CT) is a non-invasive medical diagnostic tool that has raised public concerns due to the associated health risks of radiation dose to patients. Reducing the radiation dose leads to noise artifacts, making the low-dose CT images unreliable for diagnosis. Hence, low-dose CT (LDCT) image reconstruction techniques have offered a new research area. In this study, a deep neural network is proposed, specifically a residual network (ResNet) using dilated convolution, batch normalization, and rectified linear unit (ReLU) layers with fused spatial- and channel-attention modules to enhance the quality of LDCT images. The network is optimized using the integration of per-pixel loss, perceptual loss via VGG16-net, and dissimilarity index loss. Through an ablation experiment, these functions show that they could effectively prevent edge oversmoothing, improve image texture, and preserve the structural details. Finally, comparative experiments showed that the qualitative and quantitative results of the proposed network outperform state-of-the-art denoising models such as block-matching 3D filtering (BM3D), Markovian-based patch generative adversarial network (patch-GAN), and dilated residual network with edge detection (DRL-E-MP).

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A review on Deep Learning approaches for low-dose Computed Tomography restoration

Cited by 57 publications

References 84 publications

Eliciting Auxiliary Information for Cold Start User Recommendation: A Survey

Eliciting Auxiliary Information for Cold Start User Recommendation: A Survey

Prioritisation Assessment and Robust Predictive System for Medical Equipment: A Comprehensive Strategic Maintenance Management

Low Dose CT Denoising by ResNet With Fused Attention Modules and Integrated Loss Functions

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