Improved Conditional GAN for Aerial Image Segmentation

Deep learning methods have made remarkable strides in surface defect detection. But, they heavily rely on large amount of training data, which can be a costly endeavor, especially for specific applications like steel strip surface defect detection, where acquiring and labeling large-scale data is impractical due to the rarity of certain defective categories in production environment. Hence, realistic defect image synthesis can greatly alleviate this issue. However, training image generation networks also demand substantial data, making image data augmentation merely an auxiliary effort. In this work, we propose a Generative Adversarial Network (GAN)-based image synthesis framework. We selectively extract the defect edges of the original image as well as the background texture information, and use them as network input through the spatially-adaptive (de)normalization (SPADE) module. This enriches the input information, thus significantly reducing the amount of training data for GAN network in image generation, and enhancing the background details as well as the defect boundaries in the generated images. Additionally, we introduce a novel generator loss term that balances the similarity and perceptual fidelity between synthetic and real images by constraining high-level features at different feature levels. This provides more valuable information for data augmentation in training object detection models using synthetic images. Our experimental results demonstrate the sophistication of the proposed image synthesis method and its effectiveness in data augmentation for steel strip surface defect detection tasks.

Cross-modality image estimation can be performed using generative adversarial networks (GANs). To date, SPECT image estimation from another medical imaging modality using this technique has not been considered. We evaluate the estimation of SPECT from MRI and PET, and additionally assess the necessity for cross-modality image registration for GAN training. We estimated interictal SPECT from PET and MRI as a single-channel input, and as a multi-channel input to the GAN. We collected data from 48 individuals with epilepsy and converted them to 3D isotropic images for consistence across the modalities. Training and testing data were prepared in native and template spaces. The Pix2pix framework within the GAN network was adopted. We evaluated the addition of the structural similarity index metric to the loss function in the GAN implementation. Root-mean-square error, structural similarity index, and peak signal-to-noise ratio were used to assess how well SPECT images were able to be synthesised. High quality SPECT images could be synthesised in each case. On average, the use of native space images resulted in a 5.4% percentage improvement in SSIM than the use of images registered to template space. The addition of structural similarity index metric to the GAN loss function did not result in improved synthetic SPECT images. Using PET in either the single channel or dual channel implementation led to the best results, however MRI could produce SPECT images close in quality. Synthesis of SPECT from MRI or PET can potentially reduce the number of scans needed for epilepsy patient evaluation and reduce patient exposure to radiation.

A major challenge in applying deep learning to medical imaging is the paucity of annotated data. This study explores the use of synthetic images for data augmentation to address the challenge of limited annotated data in colonoscopy lesion classification. We demonstrate that synthetic colonoscopy images generated by Generative Adversarial Network (GAN) inversion can be used as training data to improve polyp classification performance by deep learning models. We invert pairs of images with the same label to a semantically rich and disentangled latent space and manipulate latent representations to produce new synthetic images. These synthetic images maintain the same label as the input pairs. We perform image modality translation (style transfer) between white light and narrow-band imaging (NBI). We also generate realistic synthetic lesion images by interpolating between original training images to increase the variety of lesion shapes in the training dataset. Our experiments show that GAN inversion can produce multiple colonoscopy data augmentations that improve the downstream polyp classification performance by 2.7% in F1-score and 4.9% in sensitivity over other methods, including state-of-the-art data augmentation. Testing on unseen out-of-domain data also showcased an improvement of 2.9% in F1-score and 2.7% in sensitivity. This approach outperforms other colonoscopy data augmentation techniques and does not require re-training multiple generative models. It also effectively uses information from diverse public datasets, even those not specifically designed for the targeted downstream task, resulting in strong domain generalizability. Project code and model: https://github.com/DurrLab/GAN-Inversion.

Image dehazing has always been a challenging topic in image processing. The development of deep learning methods, especially the generative adversarial networks (GAN), provides a new way for image dehazing. In recent years, many deep learning methods based on GAN have been applied to image dehazing. However, GAN has two problems in image dehazing. Firstly, For haze image, haze not only reduces the quality of the image but also blurs the details of the image. For GAN network, it is difficult for the generator to restore the details of the whole image while removing the haze. Secondly, GAN model is defined as a minimax problem, which weakens the loss function. It is difficult to distinguish whether GAN is making progress in the training process. Therefore, we propose a guided generative adversarial dehazing network (GGADN). Different from other generation adversarial networks, GGADN adds a guided module on the generator. The guided module verifies the network of each layer of the generator. At the same time, the details of the map generated by each layer are strengthened. Network training is based on the pre-trained VGG feature model and L1-regularized gradient prior which is developed by new loss function parameters. From the dehazing results of synthetic images and real images, the proposed method is better than the state-of-the-art dehazing methods.

Crop yield estimation and prediction constitutes a key issue in agricultural management, particularly under the context of demographic pressure and climate change. Currently, the main challenge in estimating crop yields based on remotely sensed data and data-driven methods is how to cope with small datasets and the limited amount of annotated samples. In order to cope with small datasets and the limited amount of annotated samples and improve the accuracy of winter wheat yield estimation in the Guanzhong Plain, PR China, this study proposed a method of combining generative adversarial networks (GANs) and convolutional neural network (CNN) for comprehensive growth monitoring of winter wheat, in which the remotely sensed leaf area index (LAI), vegetation temperature condition index (VTCI) and meteorological data at four growth stages of winter wheat during 2012–2017 were generated as the inputs of multi-layer convolutional neural networks (CNNs), and GAN was employed to artificially increase the number of training samples. Then, a linear regression model between the simulated comprehensive growth monitoring (I) and the measured yields was established to estimate yields of winter wheat in the Guanzhong Plain pixel by pixel. The final results showed when GAN was used to double the size of the training samples, and the simulation values obtained by CNN based on augmented samples using GAN provided a better training (R2 = 0.95, RMSE = 0.05), validation (R2 = 0.54, RMSE = 0.16) and testing (R2 = 0.50, RMSE = 0.14) performance than that just using the original samples. The achieved best pixel-scale yield estimation accuracy of winter wheat (R2 = 0.50, RMSE = 591.46 kg/ha) in the Guanzhong Plain. These results showed that small samples can be enlarged by GAN, thus, more important features for reflecting the growth conditions and yields of winter wheat from the remotely sensed indices and meteorological indices can be extracted, and indicated that CNN accompanied with GAN could contribute a lot to the comprehensive growth monitoring and yield estimation of winter wheat and data augmentation methods are extremely useful for the application of small samples in deep learning.

Since the neural network was introduced into the super-resolution (SR) field, many SR deep models have been proposed and have achieved excellent results. However, there are two main drawbacks: one is that the methods based on the best peak-signal-to-noise ratio (PSNR) do not have enough comfortable visual quality; the other is that although the SR models based on generative adversarial network (GAN) have satisfactory visual quality, the structure of the reconstructed image has apparent defects. Therefore, according to the characteristics that human eyes are sensitive to high-frequency components in images, this article proposes an improved image SRGAN model based on high-frequency information fusion (HIFGAN). It builds a feature extraction network for high-frequency information fusion by designing a lightweight spatial attention module and improving the network architecture of enhanced super-resolution GAN (ESRGAN). It makes the generator in the GAN network have better feature recovery ability, reduces the dependence of the later training on the decider and loss function, and makes the generated image structure more consistent with the real situation. In addition, we build a high-frequency loss function to optimize the training of the generator network. Detailed experimental results show that HIFGAN performs excellently in both objective criterion evaluation and subjective visual effect. Compared with the state-of-the-art GAN-based SR networks, the reconstructed image by our model is more precise and complete in texture details.

1 - Congruence of deep learning in biomedical engineering: future prospects and challenges

This study aims to explore the feasibility of employing generative adversarial networks (GAN) to generate synthetic contrast-enhanced ultrasound (CEUS) from grayscale ultrasound images of patients with thyroid nodules while dispensing with the need for ultrasound contrast agents injection. Patients who underwent preoperative thyroid CEUS examinations between January 2020 and July 2022 were collected retrospectively. The cycle-GAN framework integrated paired and unpaired learning modules was employed to develop the non-invasive image generation process. The syntheticCEUS imageswas generated in three phases: pre-arterial, plateau, and venous. The evaluation included quantitative similarity metrics, classification performance, and qualitative assessment by radiologists. CEUS videos of 360 thyroid nodules from 314 patients (45 years ± 12 [SD]; 272 women) in the internal dataset and 202 thyroid nodules from 183 patients (46 years ± 13 [SD]; 148 women) in the external dataset were included. In the external testing dataset, quantitative analysis revealed a significant degree of similarity between real and synthetic CEUS images (structure similarity index, 0.89 ± 0.04; peak signal-to-noise ratio, 28.17 ± 2.42). Radiologists deemed 126 of 132 [95%] synthetic CEUS images diagnostically useful. The accuracy of radiologists in distinguishing between real and synthetic images was 55.6% (95% CI: 0.49, 0.63), with an AUC of 61.0% (95% CI: 0.65, 0.68). No statistically significant difference (p > 0.05) was observed when radiologists assessed peak intensity and enhancement patterns using real CEUS and synthetic CEUS. Both quantitative analysis and radiologist evaluations exhibited that synthetic CEUS images generated by generative adversarial networks were similar to real CEUS images. QuestionIt is feasible to generate synthetic thyroid contrast-enhanced ultrasound images using generative adversarial networks without ultrasound contrast agents injection. FindingsCompared to real contrast-enhanced ultrasound images, synthetic contrast-enhanced ultrasound images exhibited high similarity and image quality. Clinical relevanceThis non-invasive and intelligent transformation may reduce the requirement for ultrasound contrast agents in certain cases, particularly in scenarios where ultrasound contrast agents administration is contraindicated, such as in patients with allergies, poor tolerance, or limited access to resources.

PurposeTo develop generative adversarial networks (GANs) that synthesize realistic anterior segment optical coherence tomography (AS-OCT) images and evaluate deep learning (DL) models that are trained on real and synthetic datasets for detecting angle closure.MethodsThe GAN architecture was adopted and trained on the dataset with AS-OCT images collected from the Joint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, synthesizing open- and closed-angle AS-OCT images. A visual Turing test with two glaucoma specialists was performed to assess the image quality of real and synthetic images. DL models, trained on either real or synthetic datasets, were developed. Using the clinicians’ grading of the AS-OCT images as the reference standard, we compared the diagnostic performance of open-angle vs. closed-angle detection of DL models and the AS-OCT parameter, defined as a trabecular-iris space area 750 µm anterior to the scleral spur (TISA750), in a small independent validation dataset.ResultsThe GAN training included 28,643 AS-OCT anterior chamber angle (ACA) images. The real and synthetic datasets for DL model training have an equal distribution of open- and closed-angle images (all with 10,000 images each). The independent validation dataset included 238 open-angle and 243 closed-angle AS-OCT ACA images. The image quality of real versus synthetic AS-OCT images was similar, as assessed by the two glaucoma specialists, except for the scleral spur visibility. For the independent validation dataset, both DL models achieved higher areas under the curve compared with TISA750. Two DL models had areas under the curve of 0.97 (95% confidence interval, 0.96–0.99) and 0.94 (95% confidence interval, 0.92–0.96).ConclusionsThe GAN synthetic AS-OCT images appeared to be of good quality, according to the glaucoma specialists. The DL models, trained on all-synthetic AS-OCT images, can achieve high diagnostic performance.Translational RelevanceThe GANs can generate realistic AS-OCT images, which can also be used to train DL models.

Generative artificial intelligence, especially with regard to the generative adversarial network (GAN), is an important research area in radiology as evidenced by a number of literature reviews on the role of GAN in radiology published in the last few years. However, no review article about GAN in pediatric radiology has been published yet. The purpose of this paper is to systematically review applications of GAN in pediatric radiology, their performances, and methods for their performance evaluation. Electronic databases were used for a literature search on 6 April 2023. Thirty-seven papers met the selection criteria and were included. This review reveals that the GAN can be applied to magnetic resonance imaging, X-ray, computed tomography, ultrasound and positron emission tomography for image translation, segmentation, reconstruction, quality assessment, synthesis and data augmentation, and disease diagnosis. About 80% of the included studies compared their GAN model performances with those of other approaches and indicated that their GAN models outperformed the others by 0.1–158.6%. However, these study findings should be used with caution because of a number of methodological weaknesses. For future GAN studies, more robust methods will be essential for addressing these issues. Otherwise, this would affect the clinical adoption of the GAN-based applications in pediatric radiology and the potential advantages of GAN could not be realized widely.

Successful training of convolutional neural networks (CNNs) requires a substantial amount of data. With small datasets, networks generalize poorly. Data Augmentation techniques improve the generalizability of neural networks by using existing training data more effectively. Standard data augmentation methods, however, produce limited plausible alternative data. Generative Adversarial Networks (GANs) have been utilized to generate new data and improve the performance of CNNs. Nevertheless, data augmentation techniques for training GANs are underexplored compared to CNNs. In this work, we propose a new GAN architecture for augmentation of chest X-rays for semi-supervised detection of pneumonia and COVID-19 using generative models. We show that the proposed GAN can be used to effectively augment data and improve classification accuracy of disease in chest X-rays for pneumonia and COVID-19. We compare our augmentation GAN model with Deep Convolutional GAN and traditional augmentation methods (rotate, zoom, etc.) on two different X-ray datasets and show our GAN-based augmentation method surpasses other augmentation methods for training a GAN in detecting anomalies in X-ray images.

A Generative Adversarial Network (GAN) is an artificial intelligence (AI) technique and neural network design that trains the generator and discriminator neural networks simultaneously using adversarial training. The generator aims to generate more realistic data, whereas the discriminator aims to distinguish actual from generated data. This adversarial process makes the generator provide more realistic data. GANs are widely employed in many fields, such as style transfer, data augmentation, image generation, and, more recently, cybersecurity, where they are used to create synthetic data, mimic cyberthreats, and improve security measures. Potential applications of GANs include strengthening intrusion detection systems, enhancing anomaly detection, and simulating a variety of cyberthreats. In contrast, new complications arise when GANs are included in cybersecurity investigations. Adversarial GAN approaches expose privacy and security vulnerabilities and threats in machine learning (ML) models. The adversarial nature of GANs makes AI and ML applications more vulnerable to assaults, thereby compromising security. This dual nature highlights the necessity for a nuanced and complete approach to utilizing GANs for cybersecurity while tackling increasing problems to preserve AI/ML application robustness in the evolving threat landscape.

Cardiovascular diseases (CVDs) are a major cause of morbidity and mortality worldwide. Effective CVD treatment requires early and accurate diagnosis. CVD diagnosis and prognosis can be done using medical image analysis. In this paper, we propose a novel deep learning approach to improve diagnosis using GAN (Generative Adversarial Network), LSTM (Long Short-Term Memory), GRU (Gated Recurrent Unit), VARMA (Vector Auto Regressive Moving Average), and Deep Dyna Q Network. GAN generates synthetic medical images to train LSTM and GRU models for sequential medical image time series data analysis. VARMA models time series dataset sample temporal dependencies. Deep Dyna Q Network is used to learn the best CVD diagnosis and treatment policy. A large dataset of medical images and patient data trains the model, which is evaluated performance metrics. The proposed approach outperforms state-of-the-art CVD diagnosis methods in clinical scenarios, achieving high accuracy 95% and sensitivity. The proposed deep learning approach has great potential to improve diagnosis and treatment, cardiovascular medication, and patient lifespan in real-time scenarios.

ABSTRACTWe design a generative adversarial network (GAN)-based pencil drawing learning system for art education on large image datasets to help students study how to draw pencil drawings for images and scenes. The system generates a pencil drawing result for a natural image based on GAN. The GAN network is trained on pencil drawing big datasets containing image pairs of natural images and their corresponding pencil drawings. Using the pencil drawing learning system, students can paint pencil drawings whenever they want and for whatever they like by uploading an image of the content they want to draw and getting a pencil drawing example of the uploaded image from the system. With the returned pencil drawing, students will see the pencil drawing effect of natural scenes clearly and realize how to draw the pencil drawing for the natural scene. Besides, with students using the pencil drawing learning system, it will be convenient for teachers assigning homework to students. Teachers can know the learning demands of students by evaluating the hand-in homework and update the content correspondingly. We have conducted two user studies for evaluating the practicality of the system, and the result of the two user studies demonstrated the applicability and practicality of the system.

Surface defect recognition (SDC) is essential in intelligent manufacturing. Deep learning (DL) is a research hotspot in SDC. Limited defective samples are available in most real-world cases, which poses challenges for DL methods. Given such circumstances, generating defective samples by generative adversarial networks (GANs) is applied. However, insufficient samples and high-frequency texture details in defects make GANs very hard to train, yield mode collapse, and poor image quality, which can further impact SDC. To solve these problems, this article proposes a new GAN called contrastive GAN, which can be trained to generate diverse defects with only extremely limited samples. Specifically, a shared data augmentation (SDA) module is proposed for avoiding overfitting. Then, a feature attention matching (FAM) module is proposed to align features for improving the quality of generated images. Finally, a contrastive loss based on hypersphere is employed to constrain GANs to generate images that differ from the traditional transform. Experiments show that the proposed GAN generates defective images with higher quality and lower variance between real defects compared to other GANs. Synthetic images contribute to pretrained DL networks with accuracies of up to 95.00%–99.56% for Northeastern University (NEU) datasets of different sizes and 91.84% for printed circuit board (PCB) cases, which proves the effectiveness of the proposed method.