scholarly journals Colonoscopic image synthesis with generative adversarial network for enhanced detection of sessile serrated lesions using convolutional neural network

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Dan Yoon ◽  
Hyoun-Joong Kong ◽  
Byeong Soo Kim ◽  
Woo Sang Cho ◽  
Jung Chan Lee ◽  
...  
Keyword(s):  
High Resolution ◽  
Detection System ◽  
Morphological Characteristics ◽  
Image Synthesis ◽  
Polyp Detection ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Serrated Lesions ◽  
Low Performance ◽  
Cade System

AbstractComputer-aided detection (CADe) systems have been actively researched for polyp detection in colonoscopy. To be an effective system, it is important to detect additional polyps that may be easily missed by endoscopists. Sessile serrated lesions (SSLs) are a precursor to colorectal cancer with a relatively higher miss rate, owing to their flat and subtle morphology. Colonoscopy CADe systems could help endoscopists; however, the current systems exhibit a very low performance for detecting SSLs. We propose a polyp detection system that reflects the morphological characteristics of SSLs to detect unrecognized or easily missed polyps. To develop a well-trained system with imbalanced polyp data, a generative adversarial network (GAN) was used to synthesize high-resolution whole endoscopic images, including SSL. Quantitative and qualitative evaluations on GAN-synthesized images ensure that synthetic images are realistic and include SSL endoscopic features. Moreover, traditional augmentation methods were used to compare the efficacy of the GAN augmentation method. The CADe system augmented with GAN synthesized images showed a 17.5% improvement in sensitivity on SSLs. Consequently, we verified the potential of the GAN to synthesize high-resolution images with endoscopic features and the proposed system was found to be effective in detecting easily missed polyps during a colonoscopy.

scholarly journals Text to Realistic Image Generation with Attentional Concatenation Generative Adversarial Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Linyan Li ◽  
Yu Sun ◽  
Fuyuan Hu ◽  
Tao Zhou ◽  
Xuefeng Xi ◽  
...  
Keyword(s):  
High Resolution ◽  
Image Synthesis ◽  
Semantic Space ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Fine Grained ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Cascade Structure ◽  
High Resolution Images

In this paper, we propose an Attentional Concatenation Generative Adversarial Network (ACGAN) aiming at generating 1024 × 1024 high-resolution images. First, we propose a multilevel cascade structure, for text-to-image synthesis. During training progress, we gradually add new layers and, at the same time, use the results and word vectors from the previous layer as inputs to the next layer to generate high-resolution images with photo-realistic details. Second, the deep attentional multimodal similarity model is introduced into the network, and we match word vectors with images in a common semantic space to compute a fine-grained matching loss for training the generator. In this way, we can pay attention to the fine-grained information of the word level in the semantics. Finally, the measure of diversity is added to the discriminator, which enables the generator to obtain more diverse gradient directions and improve the diversity of generated samples. The experimental results show that the inception scores of the proposed model on the CUB and Oxford-102 datasets have reached 4.48 and 4.16, improved by 2.75% and 6.42% compared to Attentional Generative Adversarial Networks (AttenGAN). The ACGAN model has a better effect on text-generated images, and the resulting image is closer to the real image.

scholarly journals Realistic high-resolution body CT image synthesis using progressive growing generative adversarial network: A Visual Turing Test (Preprint)

2020 ◽  
Author(s):  
Ho Young Park ◽  
Hyun-Jun Bae ◽  
Gil-Sun Hong ◽  
JiHye Yun ◽  
Sung Won Park ◽  
...  
Keyword(s):  
High Resolution ◽  
Image Synthesis ◽  
Turing Test ◽  
Ct Images ◽  
Body Images ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Image Set ◽  
Validation Set ◽  
Body Ct

BACKGROUND Generative Adversarial Network (GAN)-based synthetic images can be viable solutions to current supervised deep learning challenges. However, generating highly realistic images is a prerequisite for these approaches. OBJECTIVE We investigated and validated the unsupervised synthesis of highly realistic body CT images using a progressive growing GAN (PGGAN) trained to learn the probability distribution of normal data. METHODS We trained the PGGAN using 11 755 body CT scans. Ten radiologists then evaluated the results in a binary approach using an independent validation set of 300 images (150 real, 150 synthetic) to judge the authenticity of each image. RESULTS Mean accuracy for the entire image set was low (59.4%), and accuracy among three reader groups with different experience levels was not significantly different (58.0% - 60.5%, P = 0.36). Inter-reader agreements were poor (κ = 0.11) for the entire image set. In subgroup analysis, the discrepancies between real and synthetic CT images occurred mainly in the thoracoabdominal junction and in anatomical details. CONCLUSIONS The GAN can synthesize highly realistic high-resolution body CT images, which are indistinguishable from real images; however, it has limitations in generating body images in the thoracoabdominal junction and lacks accuracy in anatomical details.

scholarly journals CDL-GAN: Contrastive Distance Learning Generative Adversarial Network for Image Generation

2021 ◽  
Vol 11 (4) ◽  
pp. 1380
Author(s):  
Yingbo Zhou ◽  
Pengcheng Zhao ◽  
Weiqin Tong ◽  
Yongxin Zhu
Keyword(s):  
Distance Learning ◽  
Feature Learning ◽  
Image Synthesis ◽  
Image Feature ◽  
Generative Adversarial Networks ◽  
Image Generation ◽  
Generative Adversarial Network ◽  
Feature Representations ◽  
Adversarial Network ◽  
Public Datasets

While Generative Adversarial Networks (GANs) have shown promising performance in image generation, they suffer from numerous issues such as mode collapse and training instability. To stabilize GAN training and improve image synthesis quality with diversity, we propose a simple yet effective approach as Contrastive Distance Learning GAN (CDL-GAN) in this paper. Specifically, we add Consistent Contrastive Distance (CoCD) and Characteristic Contrastive Distance (ChCD) into a principled framework to improve GAN performance. The CoCD explicitly maximizes the ratio of the distance between generated images and the increment between noise vectors to strengthen image feature learning for the generator. The ChCD measures the sampling distance of the encoded images in Euler space to boost feature representations for the discriminator. We model the framework by employing Siamese Network as a module into GANs without any modification on the backbone. Both qualitative and quantitative experiments conducted on three public datasets demonstrate the effectiveness of our method.

scholarly journals Generating High-Resolution Climate Prediction through Generative Adversarial Network

2020 ◽  
Vol 174 ◽  
pp. 123-127
Author(s):  
Jianxin Cheng ◽  
Jin Liu ◽  
Zhou Xu ◽  
Chenkai Shen ◽  
Qiuming Kuang
Keyword(s):  
High Resolution ◽  
Climate Prediction ◽  
Generative Adversarial Network ◽  
Adversarial Network
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