Semi Supervised Generative Adversarial Network for Automated Glaucoma Diagnosis with Stacked Discriminator Models

2021 ◽  
Vol 11 (5) ◽  
pp. 1334-1340
Author(s):  
K. Gokul Kannan ◽  
T. R. Ganesh Babu
Keyword(s):  
Network Architecture ◽  
Super Resolution ◽  
Unsupervised Classification ◽  
Learning Task ◽  
Generative Models ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Glaucoma Diagnosis ◽  
Image Translation

Generative Adversarial Network (GAN) is neural network architecture, widely used in many computer vision applications such as super-resolution image generation, art creation and image to image translation. A conventional GAN model consists of two sub-models; generative model and discriminative model. The former one generates new samples based on an unsupervised learning task, and the later one classifies them into real or fake. Though GAN is most commonly used for training generative models, it can be used for developing a classifier model. The main objective is to extend the effectiveness of GAN into semi-supervised learning, i.e., for the classification of fundus images to diagnose glaucoma. The discriminator model in the conventional GAN is improved via transfer learning to predict n + 1 classes by training the model for both supervised classification (n classes) and unsupervised classification (fake or real). Both models share all feature extraction layers and differ in the output layers. Thus any update in one of the model will impact both models. Results show that the semi-supervised GAN performs well than a standalone Convolution Neural Networks (CNNs) model.

scholarly journals CBNWI-50: A Deep Learning Bird Dataset for Image Translation and Resolution Improvement using Generative Adversarial Network

2019 ◽  
Vol 8 (9S) ◽  
pp. 91-102
Keyword(s):  
Deep Learning ◽  
Super Resolution ◽  
Generative Adversarial Networks ◽  
Western India ◽  
Generative Adversarial Network ◽  
Style Transfer ◽  
Adversarial Network ◽  
Image Translation ◽  
Common Birds ◽  
Single Image Super Resolution

Generative Adversarial Networks have gained prominence in a short span of time as they can synthesize images from latent noise by minimizing the adversarial cost function. New variants of GANs have been developed to perform specific tasks using state-of-the-art GAN models, like image translation, single image super resolution, segmentation, classification, style transfer etc. However, a combination of two GANs to perform two different applications in one model has been sparsely explored. Hence, this paper concatenates two GANs and aims to perform Image Translation using Cycle GAN model on bird images and improve their resolution using SRGAN. During the extensive survey, it is observed that most of the deep learning databases on Aves were built using the new world species (i.e. species found in North America). Hence, to bridge this gap, a new Ave database, 'Common Birds of North - Western India' (CBNWI-50), is also proposed in this work.

scholarly journals MapGAN: An Intelligent Generation Model for Network Tile Maps

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3119 ◽  
Author(s):  
Jingtao Li ◽  
Zhanlong Chen ◽  
Xiaozhen Zhao ◽  
Lijia Shao
Keyword(s):  
Image Inpainting ◽  
Super Resolution ◽  
Image Synthesis ◽  
Generative Adversarial Networks ◽  
Great Success ◽  
Generation Model ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Image Translation ◽  
Map Generation

In recent years, the generative adversarial network (GAN)-based image translation model has achieved great success in image synthesis, image inpainting, image super-resolution, and other tasks. However, the images generated by these models often have problems such as insufficient details and low quality. Especially for the task of map generation, the generated electronic map cannot achieve effects comparable to industrial production in terms of accuracy and aesthetics. This paper proposes a model called Map Generative Adversarial Networks (MapGAN) for generating multitype electronic maps accurately and quickly based on both remote sensing images and render matrices. MapGAN improves the generator architecture of Pix2pixHD and adds a classifier to enhance the model, enabling it to learn the characteristics and style differences of different types of maps. Using the datasets of Google Maps, Baidu maps, and Map World maps, we compare MapGAN with some recent image translation models in the fields of one-to-one map generation and one-to-many domain map generation. The results show that the quality of the electronic maps generated by MapGAN is optimal in terms of both intuitive vision and classic evaluation indicators.

scholarly journals RBDN: Residual Bottleneck Dense Network for Image Super-Resolution

2021 ◽  
Author(s):  
Zeyu An ◽  
Junyuan Zhang ◽  
Ziyu Sheng ◽  
Xuanhe Er ◽  
Junjie Lv
Keyword(s):  
Network Architecture ◽  
Super Resolution ◽  
Dense Network ◽  
Single Image ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Network Building ◽  
Image Super Resolution ◽  
Feature Values ◽  
Single Image Super Resolution

Abstract Recent studies have shown that Super-Resolution Generative Adversarial Network (SRGAN) can significantly improve the quality of single-image super-resolution. However, the existing SRGAN approaches also have drawbacks, such as inadequate of features utilization, huge number of parameters and poor scalability. To further enhance the visual quality, we thoroughly study three key components of SRGAN: network architecture, adversarial loss and perceptual loss, and propose a DenseNet with Residual-in-Residual Bottleneck Block (RRBB) named as Residual Bottleneck Dense Network (RBDN) for single-image super-resolution. In particular, RBDN combines ResNet and DenseNet with different roles, in which ResNet refines feature values by addition and DenseNet memorizes feature values by concatenation. Specifically, the DenseNet adopts the Bottleneck structure to reduce the network parameters and improve the convergence rate. In addition, the proposed RRBB, as the basic network building unit, removes the batch normalization (BN) layer and employs the ELU function to reduce the opposite effects in the absence of BN. In this way, RBDN can enjoy the merits of the sufficient feature value refined by residual groups and the refined feature value memorized by dense connections, thus achieving better performance compared with most current residual blocks.

scholarly journals Super-Resolution of Remote Sensing Images via a Dense Residual Generative Adversarial Network

2019 ◽  
Vol 11 (21) ◽  
pp. 2578 ◽  
Author(s):  
Wen Ma ◽  
Zongxu Pan ◽  
Feng Yuan ◽  
Bin Lei
Keyword(s):  
Remote Sensing ◽  
Network Architecture ◽  
Super Resolution ◽  
Objective Evaluation ◽  
Generative Adversarial Networks ◽  
Remote Sensing Images ◽  
Generative Adversarial Network ◽  
Memory Mechanism ◽  
Adversarial Network ◽  
Sensing Applications

Single image super-resolution (SISR) has been widely studied in recent years as a crucial technique for remote sensing applications. In this paper, a dense residual generative adversarial network (DRGAN)-based SISR method is proposed to promote the resolution of remote sensing images. Different from previous super-resolution (SR) approaches based on generative adversarial networks (GANs), the novelty of our method mainly lies in the following factors. First, we made a breakthrough in terms of network architecture to improve performance. We designed a dense residual network as the generative network in GAN, which can make full use of the hierarchical features from low-resolution (LR) images. We also introduced a contiguous memory mechanism into the network to take advantage of the dense residual block. Second, we modified the loss function and altered the model of the discriminative network according to the Wasserstein GAN with a gradient penalty (WGAN-GP) for stable training. Extensive experiments were performed using the NWPU-RESISC45 dataset, and the results demonstrated that the proposed method outperforms state-of-the-art methods in terms of both objective evaluation and subjective perspective.

An Automatic Classification of the Early Osteonecrosis of Femoral Head with Deep Learning

2019 ◽  
Vol 15 ◽  
Author(s):  
Liyang Zhu ◽  
Jungang Han ◽  
Renwen Guo ◽  
Dong Wu ◽  
Qiang Wei ◽  
...  
Keyword(s):  
Deep Learning ◽  
Femoral Head ◽  
Unsupervised Classification ◽  
Femur Head ◽  
Generative Adversarial Network ◽  
Osteonecrosis Of Femoral Head ◽  
Adversarial Network ◽  
Convolutional Autoencoder ◽  
Ct Slices

Background: Osteonecrosis of Femoral Head (ONFH) is a common complication in orthopaedics, wherein femoral structures are usually damaged due to the impairment or interruption of femoral head blood supply. Aim: In this study, we propose an automatic approach for the classification of the early ONFH with deep learning. Methods: We first classify all femoral CT slices according to their spatial locations with the Convolutional Neural Network (CNN). So, all CT slices are divided into upper, middle or lower segments of femur head. Then the femur head areas can be segmented with the Conditional Generative Adversarial Network (CGAN) for each part. The Convolutional Autoencoder is employed to reduce dimensions and extract features of femur head, and finally K-means clustering is used for an unsupervised classification of the early ONFH. Results: To invalidate the effectiveness of the proposed approach, we carry out the experiments on the dataset with 120 patients. The experimental results show that the segmentation accuracy is higher than 95%. The Convolutional Autoencoder can reduce the dimension of data, the Peak Signal-to-Noise Ratios (PSNRs) are better than 34dB for inputs and outputs. Meanwhile, there is a great intra-category similarity, and a significant inter-category difference. Conclusion: The research on the classification of the early ONFH has a valuable clinical merit, and hopefully it can assist physicians to apply more individualized treatment for patient.

scholarly journals Super-Resolution Reconstruction of Underwater Image Based on Image Sequence Generative Adversarial Network

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Li Li ◽  
Zijia Fan ◽  
Mingyang Zhao ◽  
Xinlei Wang ◽  
Zhongyang Wang ◽  
...  
Keyword(s):  
Network Architecture ◽  
Signal To Noise Ratio ◽  
Super Resolution ◽  
Structural Similarity ◽  
Image Sequence ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Underwater Image ◽  
Dual Generator ◽  
The Stability

Since the underwater image is not clear and difficult to recognize, it is necessary to obtain a clear image with the super-resolution (SR) method to further study underwater images. The obtained images with conventional underwater image super-resolution methods lack detailed information, which results in errors in subsequent recognition and other processes. Therefore, we propose an image sequence generative adversarial network (ISGAN) method for super-resolution based on underwater image sequences collected by multifocus from the same angle, which can obtain more details and improve the resolution of the image. At the same time, a dual generator method is used in order to optimize the network architecture and improve the stability of the generator. The preprocessed images are, respectively, passed through the dual generator, one of which is used as the main generator to generate the SR image of sequence images, and the other is used as the auxiliary generator to prevent the training from crashing or generating redundant details. Experimental results show that the proposed method can be improved on both peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) compared to the traditional GAN method in underwater image SR.

scholarly journals Disentangled generative adversarial network for low-dose CT

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Wenchao Du ◽  
Hu Chen ◽  
Hongyu Yang ◽  
Yi Zhang
Keyword(s):  
Network Architecture ◽  
Low Dose ◽  
Noise Suppression ◽  
State Of The Art ◽  
Visual Quality ◽  
Ct Images ◽  
Generative Adversarial Network ◽  
Low Dose Ct ◽  
Adversarial Network ◽  
Suppression Method

AbstractGenerative adversarial network (GAN) has been applied for low-dose CT images to predict normal-dose CT images. However, the undesired artifacts and details bring uncertainty to the clinical diagnosis. In order to improve the visual quality while suppressing the noise, in this paper, we mainly studied the two key components of deep learning based low-dose CT (LDCT) restoration models—network architecture and adversarial loss, and proposed a disentangled noise suppression method based on GAN (DNSGAN) for LDCT. Specifically, a generator network, which contains the noise suppression and structure recovery modules, is proposed. Furthermore, a multi-scaled relativistic adversarial loss is introduced to preserve the finer structures of generated images. Experiments on simulated and real LDCT datasets show that the proposed method can effectively remove noise while recovering finer details and provide better visual perception than other state-of-the-art methods.

scholarly journals An Imbalanced Image Classification Method for the Cell Cycle Phase

Information ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 249
Author(s):  
Xin Jin ◽  
Yuanwen Zou ◽  
Zhongbing Huang
Keyword(s):  
Cell Cycle ◽  
Deep Learning ◽  
Image Classification ◽  
Classification Accuracy ◽  
Data Augmentation ◽  
Cycle Phase ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Cellular Life

The cell cycle is an important process in cellular life. In recent years, some image processing methods have been developed to determine the cell cycle stages of individual cells. However, in most of these methods, cells have to be segmented, and their features need to be extracted. During feature extraction, some important information may be lost, resulting in lower classification accuracy. Thus, we used a deep learning method to retain all cell features. In order to solve the problems surrounding insufficient numbers of original images and the imbalanced distribution of original images, we used the Wasserstein generative adversarial network-gradient penalty (WGAN-GP) for data augmentation. At the same time, a residual network (ResNet) was used for image classification. ResNet is one of the most used deep learning classification networks. The classification accuracy of cell cycle images was achieved more effectively with our method, reaching 83.88%. Compared with an accuracy of 79.40% in previous experiments, our accuracy increased by 4.48%. Another dataset was used to verify the effect of our model and, compared with the accuracy from previous results, our accuracy increased by 12.52%. The results showed that our new cell cycle image classification system based on WGAN-GP and ResNet is useful for the classification of imbalanced images. Moreover, our method could potentially solve the low classification accuracy in biomedical images caused by insufficient numbers of original images and the imbalanced distribution of original images.

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