Image Processing for Low-Dose CT via Novel Anisotropic Fourth-Order Diffusion Model

Wang, Lei; Liu, Yi; Wu, Rui; Liu, Yuhang; Yan, Rongbiao; Ren, Shilei; Gui, Zhiguo

doi:10.1109/access.2022.3172975

Cited by 5 publications

(2 citation statements)

References 31 publications

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“…Recent works in the medical field follow this approach obtaining better results than other state-of-the-art works. Jalal et al [ 178 ] and Chung and Chul [ 179 ] make use of diffusion models to improve the quality of MR scans, while Wang et al [ 180 ] apply it to low-dose CT images, thus being able to improve their quality and level of detail. In future works, hybrid models could take benefits from Diffusion models merging it with other generative alternatives previously presented in this work.…”

Section: Discussionmentioning

confidence: 99%

A survey on deep learning applied to medical images: from simple artificial neural networks to generative models

Celard

Iglesias

Sorribes-Fdez

et al. 2022

Neural Comput & Applic

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Deep learning techniques, in particular generative models, have taken on great importance in medical image analysis. This paper surveys fundamental deep learning concepts related to medical image generation. It provides concise overviews of studies which use some of the latest state-of-the-art models from last years applied to medical images of different injured body areas or organs that have a disease associated with (e.g., brain tumor and COVID-19 lungs pneumonia). The motivation for this study is to offer a comprehensive overview of artificial neural networks (NNs) and deep generative models in medical imaging, so more groups and authors that are not familiar with deep learning take into consideration its use in medicine works. We review the use of generative models, such as generative adversarial networks and variational autoencoders, as techniques to achieve semantic segmentation, data augmentation, and better classification algorithms, among other purposes. In addition, a collection of widely used public medical datasets containing magnetic resonance (MR) images, computed tomography (CT) scans, and common pictures is presented. Finally, we feature a summary of the current state of generative models in medical image including key features, current challenges, and future research paths.

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Section: Discussionmentioning

confidence: 99%

A survey on deep learning applied to medical images: from simple artificial neural networks to generative models

Celard

Iglesias

Sorribes-Fdez

et al. 2022

Neural Comput & Applic

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show abstract

“…Text image generation models based on diffusion models are not only small in number of parameters, but also generate high quality images. Diffusion models [2,3] use Markov chains mapped to latent space. these models can be divided into two processes: a forward process, which gradually scrambles the data distribution by adding noise on multiple scales.…”

Section: Related Studiesmentioning

confidence: 99%

Research on cross-modal hashing method based on diffusion mode

Zhong

2023

Eighth International Conference on Electronic Technology and Information Science (ICETIS 2023)

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For achieving fast and flexible retrieval across heterogeneous modalities, unsupervised is more flexible and easy to use than supervised methods, of which the unsupervised method GAN is the most popular. However, GAN has been suffering from the problems of lack of diversity in generated samples, debugging difficulties and training instability. A cross-modal hashing method based on a diffusion model is proposed in the paper. Specifically: (1) For the first time, the diffusion model is applied to the field of cross-modal retrieval, targeting three modalities for mutual retrieval. (2) The combination of adversarial network GAN and diffusion model improves the sample quality and sample diversity, and ameliorates the problems of complex GAN debugging and unstable training. The effectiveness of the proposed method is demonstrated through experiments on three datasets and comparison with state-of-the-art methods.

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Industrial CT image reconstruction for faster scanning through U-Net++ with hybrid attention and loss function

Long,

Tan,

et al. 2024

Nondestructive Testing and Evaluation

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Image Processing for Low-Dose CT via Novel Anisotropic Fourth-Order Diffusion Model

Cited by 5 publications

References 31 publications

A survey on deep learning applied to medical images: from simple artificial neural networks to generative models

A survey on deep learning applied to medical images: from simple artificial neural networks to generative models

Research on cross-modal hashing method based on diffusion mode

Industrial CT image reconstruction for faster scanning through U-Net++ with hybrid attention and loss function

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