Low-dose chest X-ray image super-resolution using generative adversarial nets with spectral normalization

Xu, Liming; Zeng, Xiaoyang; Huang, Zhiwei; Li, Weisheng; Zhang, He

doi:10.1016/j.bspc.2019.101600

Cited by 43 publications

(32 citation statements)

References 14 publications

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“…The largest singular vector can be approached to Lipschitz constant. Xu et al [20] use spectral normalization for super-resolution of low-dose X-ray images. The spectral normalization is used to normalize the weight matrices in the discriminator which controls the Lipschitz constant to 1.…”

Section: Solutions To the Problem 1) Regularizationmentioning

confidence: 99%

“…The mode collapse problem occurs when the generator produces similar output images while taking different input features. In the domain of biomedical imaging, the mode collapse problem of GANs has been addressed by using minibatch discrimination [17], skip connections [18], VAEGAN [19], varying layers of generator and discriminator [3], spectral normalization [20], perceptual image hashing [21], Gaussian mixture model as generator [22], discriminator with conditional information vector [23], and self attention mechanism [24]. The non-convergence problem occurs due to the lack of GAN's ability to reach Nash equilibrium.…”

Section: Introductionmentioning

confidence: 99%

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A Survey on Training Challenges in Generative Adversarial Networks for Biomedical Image Analysis

Saad¹,

O’Reilly²,

Rehmani³

2022

Preprint

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In biomedical image analysis, the applicability of deep learning methods is directly impacted by the quantity of image data available. This is due to deep learning models requiring large image datasets to provide high-level performance. Generative Adversarial Networks (GANs) have been widely utilized to address data limitations through the generation of synthetic biomedical images. GANs consist of two models. The generator, a model that learns how to produce synthetic images based on the feedback it receives. The discriminator, a model that classifies an image as synthetic or real and provides feedback to the generator. Throughout the training process, a GAN can experience several technical challenges that impede the generation of suitable synthetic imagery. First, the mode collapse problem whereby the generator either produces an identical image or produces a uniform image from distinct input features. Second, the non-convergence problem whereby the gradient descent optimizer fails to reach a Nash equilibrium. Thirdly, the vanishing gradient problem whereby unstable training behavior occurs due to the discriminator achieving optimal classification performance resulting in no meaningful feedback being provided to the generator. These problems result in the production of synthetic imagery that is blurry, unrealistic, and less diverse. To date, there has been no survey article outlining the impact of these technical challenges in the context of the biomedical imagery domain. This work presents a review and taxonomy based on solutions to the training problems of GANs in the biomedical imaging domain. This survey highlights important challenges and outlines future research directions about the training of GANs in the domain of biomedical imagery.

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Section: Solutions To the Problem 1) Regularizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Survey on Training Challenges in Generative Adversarial Networks for Biomedical Image Analysis

Saad¹,

O’Reilly²,

Rehmani³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The normalization is applied in the generator and discriminator simultaneously. Most recently SN was incorporated into GAN for improving low dose chest X-ray image resolution [26] and multi-modal neuroimage synthesis [13]. Self-Attention (SA) Module: SA module calculates the attention value between local pixel regions and helps to model global correlation in a wider range.…”

Section: Stage-i Ganmentioning

confidence: 99%

Realistic Ultrasound Image Synthesis for Improved Classification of Liver Disease

Che,

Ramanathan,

Foran

et al. 2021

Preprint

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With the success of deep learning-based methods applied in medical image analysis, convolutional neural networks (CNNs) have been investigated for classifying liver disease from ultrasound (US) data. However, the scarcity of available large-scale labeled US data has hindered the success of CNNs for classifying liver disease from US data. In this work, we propose a novel generative adversarial network (GAN) architecture for realistic diseased and healthy liver US image synthesis. We adopt the concept of stacking to synthesize realistic liver US data. Quantitative and qualitative evaluation is performed on 550 in-vivo Bmode liver US images collected from 55 subjects. We also show that the synthesized images, together with real in vivo data, can be used to significantly improve the performance of traditional CNN architectures for Nonalcoholic fatty liver disease (NAFLD) classification.

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“…The low-resolution image is the result of the warping, blurring, and subsampling performed on the high-resolution image x during the acquisition process. By considering the above effects on modeling the imaging system, the imaging system [30] can be represented by equation (1).…”

Section: Related Work a Forward Imaging System Modelmentioning

confidence: 99%

Self-Organized Maps and High-Frequency Image Detail for MRI Image Enhancement

Ahmadian

Reza-Alikhani

2021

IEEE Access

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Magnetic resonance imaging (MRI) is a medical technology that uses powerful magnets, radio waves, and a computer to produce images of the body's internal organs. The patient should be quiet and motionless during the scanning period, as unavoidable movements, such as breathing and heart rate, cause motion artifacts in the image, which cause contrast instability and low-resolution MRI images. Imaging in the clinical setting is performed at low resolution because the scanning time for high-resolution MR imaging would be very long and cumbersome, and imaging is also very expensive. Learning-based image superresolution methods can reconstruct MR images with optimal quality and resolution. However, these methods have problems such as the inability to find the intrinsic relationship between low-and highresolution image patches in the training dictionary, specification of the proper amount of error in the training and testing stage due to variability in MR image contrast, inability to reconstruct objects by smoothed edges, and use of the backpropagation method in updating their weight vectors. In this paper, we propose a new superresolution method according to competitive learning-based approaches to overcome the problems of previous superresolution methods, which do not have the problems and complexities of those methods. The proposed method includes self-organizing maps as a preprocessor, the nearest neighbor algorithm as a classifier, and a high-frequency filter as a high-frequency image detail extractor. We constructed a single external dictionary from a combination of low-resolution and high-resolution feature patches and trained our SOM network. Next, we reconstruct the high-resolution image by converting the low-resolution input image into feature patch vectors, and for each vector, we find all corresponding neurons in the network and retrieve all their training feature vectors. Next, we train the nearest neighbor algorithm with the recovered vectors plus the input vector and find the best similarity vector to the input vector. After finding all the best similarity vectors to the input vectors, we reconstruct our high superresolution image. The proposed image superresolution method in practical experiments was trained, tested, and evaluated by the Div2k dataset and compared with other traditional and state-of-the-art image enhancement methods on various datasets, such as SET5, SET14, BSDS100, and URBAN100, and presented better results with higher accuracy and quality than traditional and state-of-the-art methods, both visually compared to each other by human and computational benchmarks, such as the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), to compare image superresolution algorithms. This method is best for reconstructing highresolution magnetic resonance images that require high-frequency details and sharp edges with a smooth slope of the imaging objects in their structures. The execution time of the proposed algorithm is slower than that of the other algorithms, so we use GPU har...

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Low-dose chest X-ray image super-resolution using generative adversarial nets with spectral normalization

Cited by 43 publications

References 14 publications

A Survey on Training Challenges in Generative Adversarial Networks for Biomedical Image Analysis

A Survey on Training Challenges in Generative Adversarial Networks for Biomedical Image Analysis

Realistic Ultrasound Image Synthesis for Improved Classification of Liver Disease

Self-Organized Maps and High-Frequency Image Detail for MRI Image Enhancement

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