Dilated Convolution ResNet with Boosting Attention Modules and Combined Loss Functions for LDCT Image Denoising

Marcos, Luella; Quint, Franz; Babyn, Paul; Alirezaie, Javad

doi:10.1109/embc48229.2022.9870993

Cited by 7 publications

(10 citation statements)

References 10 publications

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“…The choice of validation method depends on factors such as dataset size, data distribution, and the model's complexity. K-fold cross-validation is the most popular validation method typically used for supervised learning tasks where there are labeled data and a target variable to predict, including single fold, 68,71,72 5-fold, [97][98][99][100]102 and 10-fold cross-validation. [128][129][130][131] In general, the performance results from higher iterations can be averaged to obtain a more reliable estimation of the model's performance.…”

Section: Validation Methodsmentioning

confidence: 99%

“…The predominant DL models for CT denoising are GANs and CNNs. As shown in Figure 2a, out of 99 publications examined, 61 studies use the models based on CNN, 59–119 while 30 studies are based on GAN 120–149 . Additionally, two studies adopt Transformer‐based approaches 150,151 .…”

Section: Dl‐based Noise Reduction Methodsmentioning

confidence: 99%

“…ResNet‐based CT denoising methods use a network architecture that can learn to remove noise while preserving important image features. Attention‐based Networks employ attention mechanisms to selectively focus on important image features while denoising the input image 72 …”

Section: Dl‐based Noise Reduction Methodsmentioning

confidence: 99%

“…Supervised DL‐based methods have been shown to produce high‐quality denoised CT images, and they are effective at removing various types of noise patterns. Out of 99 studies reviewed, 64 studies 42,59–68,71,72,74,76,77,79,80,83,84,86,88–93,97–104 , 106–109,111–119,123–126,132,134,137–139,142,147,153,159,161–163 apply supervised training method. CNNs are a common type of supervised DL‐based model used for CT image denoising.…”

Section: Training Validation and Evaluationmentioning

confidence: 99%

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CT image denoising methods for image quality improvement and radiation dose reduction

Sadia,

Chen,

Zhang

2024

J Applied Clin Med Phys

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With the ever‐increasing use of computed tomography (CT), concerns about its radiation dose have become a significant public issue. To address the need for radiation dose reduction, CT denoising methods have been widely investigated and applied in low‐dose CT images. Numerous noise reduction algorithms have emerged, such as iterative reconstruction and most recently, deep learning (DL)‐based approaches. Given the rapid advancements in Artificial Intelligence techniques, we recognize the need for a comprehensive review that emphasizes the most recently developed methods. Hence, we have performed a thorough analysis of existing literature to provide such a review. Beyond directly comparing the performance, we focus on pivotal aspects, including model training, validation, testing, generalizability, vulnerability, and evaluation methods. This review is expected to raise awareness of the various facets involved in CT image denoising and the specific challenges in developing DL‐based models.

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Section: Validation Methodsmentioning

confidence: 99%

Section: Dl‐based Noise Reduction Methodsmentioning

confidence: 99%

Section: Dl‐based Noise Reduction Methodsmentioning

confidence: 99%

Section: Training Validation and Evaluationmentioning

confidence: 99%

See 2 more Smart Citations

CT image denoising methods for image quality improvement and radiation dose reduction

Sadia,

Chen,

Zhang

2024

J Applied Clin Med Phys

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“…The computational load grows when the filter size is increased, adding additional parameters. ResNet [7][8] introduces a skip connection to fit the input of the previous layer to the next layer without modifying the input. ResNet is also a deep convolutional neural network composed of residual blocks.…”

Section: Related Workmentioning

confidence: 99%

Image Denoising based on Deep Learning

Li¹

2023

HSET

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Image denoising has always been one of the research hotspots in the field of image processing, which aims to remove the noise from the imaging device or external noise environment and other interfering factors in the image to restore the noisy image to the original clean and noise-free image. Mature algorithms and machine learning techniques have been developed previously for different application situations and specific computer vision works. Image denoising based on deep learning can adaptively learn image content and is suitable for image denoising tasks in high-noise environments. This paper builds a model based on the representative image denoising algorithm DnCNN, and discusses the performance difference with other denoising algorithms. All the results show the new methods are usually more efficient than traditional ones which can process pictures that are under more complex conditions.

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SEANet: Rethinking Skip-Connections Design in Encoder-Decoder Networks via Synergistic Spatial-Spectral Fusion for LDCT Denoising

Das,

Gorade,

Mahapatra

et al. 2024

Lecture Notes in Computer Science

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Dilated Convolution ResNet with Boosting Attention Modules and Combined Loss Functions for LDCT Image Denoising

Cited by 7 publications

References 10 publications

CT image denoising methods for image quality improvement and radiation dose reduction

CT image denoising methods for image quality improvement and radiation dose reduction

Image Denoising based on Deep Learning

SEANet: Rethinking Skip-Connections Design in Encoder-Decoder Networks via Synergistic Spatial-Spectral Fusion for LDCT Denoising

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