scholarly journals GANai: Standardizing CT Images using Generative Adversarial Network with Alternative Improvement

2018 ◽  
Author(s):  
Gongbo Liang ◽  
Sajjad Fouladvand ◽  
Jie Zhang ◽  
Michael A. Brooks ◽  
Nathan Jacobs ◽  
...  
Keyword(s):  
Large Scale ◽  
Model Performance ◽  
Image Synthesis ◽  
Ct Images ◽  
Image Features ◽  
Training Data ◽  
Ct Image ◽  
Generative Adversarial Network ◽  
Training Strategy ◽  
Adversarial Network

AbstractComputed tomography (CT) is a widely-used diag-reproducibility regarding radiomic features, such as intensity, nostic image modality routinely used for assessing anatomical tissue characteristics. However, non-standardized imaging pro-tocols are commonplace, which poses a fundamental challenge in large-scale cross-center CT image analysis. One approach to address the problem is to standardize CT images using generative adversarial network models (GAN). GAN learns the data distribution of training images and generate synthesized images under the same distribution. However, existing GAN models are not directly applicable to this task mainly due to the lack of constraints on the mode of data to generate. Furthermore, they treat every image equally, but in real applications, some images are more difficult to standardize than the others. All these may lead to the lack-of-detail problem in CT image synthesis. We present a new GAN model called GANai to mitigate the differences in radiomic features across CT images captured using non-standard imaging protocols. Given source images, GANai composes new images by specifying a high-level goal that the image features of the synthesized images should be similar to those of the standard images. GANai introduces an alternative improvement training strategy to alternatively and steadily improve model performance. The new training strategy enables a series of technical improvements, including phase-specific loss functions, phase-specific training data, and the adoption of ensemble learning, leading to better model performance. The experimental results show that GANai is significantly better than the existing state-of-the-art image synthesis algorithms on CT image standardization. Also, it significantly improves the efficiency and stability of GAN model training.

scholarly journals An Indirect Multimodal Image Registration and Completion Method Guided by Image Synthesis

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Huan Yang ◽  
Pengjiang Qian ◽  
Chao Fan
Keyword(s):  
Image Registration ◽  
Auxiliary Information ◽  
Image Synthesis ◽  
Training Data ◽  
Synthesis Methods ◽  
Generative Adversarial Network ◽  
Registration Method ◽  
Multimodal Image Registration ◽  
Adversarial Network ◽  
Ct And Mri

Multimodal registration is a challenging task due to the significant variations exhibited from images of different modalities. CT and MRI are two of the most commonly used medical images in clinical diagnosis, since MRI with multicontrast images, together with CT, can provide complementary auxiliary information. The deformable image registration between MRI and CT is essential to analyze the relationships among different modality images. Here, we proposed an indirect multimodal image registration method, i.e., sCT-guided multimodal image registration and problematic image completion method. In addition, we also designed a deep learning-based generative network, Conditional Auto-Encoder Generative Adversarial Network, called CAE-GAN, combining the idea of VAE and GAN under a conditional process to tackle the problem of synthetic CT (sCT) synthesis. Our main contributions in this work can be summarized into three aspects: (1) We designed a new generative network called CAE-GAN, which incorporates the advantages of two popular image synthesis methods, i.e., VAE and GAN, and produced high-quality synthetic images with limited training data. (2) We utilized the sCT generated from multicontrast MRI as an intermediary to transform multimodal MRI-CT registration into monomodal sCT-CT registration, which greatly reduces the registration difficulty. (3) Using normal CT as guidance and reference, we repaired the abnormal MRI while registering the MRI to the normal CT.

scholarly journals Attention-Aware Adversarial Network for Person Re-Identification

2019 ◽  
Vol 9 (8) ◽  
pp. 1550 ◽  
Author(s):  
Aihong Shen ◽  
Huasheng Wang ◽  
Junjie Wang ◽  
Hongchen Tan ◽  
Xiuping Liu ◽  
...  
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Data Augmentation ◽  
Fundamental Problem ◽  
Training Data ◽  
Specific Data ◽  
Training Strategy ◽  
Adversarial Network ◽  
Benchmark Datasets ◽  
Adversarial Training

Person re-identification (re-ID) is a fundamental problem in the field of computer vision. The performance of deep learning-based person re-ID models suffers from a lack of training data. In this work, we introduce a novel image-specific data augmentation method on the feature map level to enforce feature diversity in the network. Furthermore, an attention assignment mechanism is proposed to enforce that the person re-ID classifier focuses on nearly all important regions of the input person image. To achieve this, a three-stage framework is proposed. First, a baseline classification network is trained for person re-ID. Second, an attention assignment network is proposed based on the baseline network, in which the attention module learns to suppress the response of the current detected regions and re-assign attentions to other important locations. By this means, multiple important regions for classification are highlighted by the attention map. Finally, the attention map is integrated in the attention-aware adversarial network (AAA-Net), which generates high-performance classification results with an adversarial training strategy. We evaluate the proposed method on two large-scale benchmark datasets, including Market1501 and DukeMTMC-reID. Experimental results show that our algorithm performs favorably against the state-of-the-art methods.

scholarly journals Deep CT to MR Synthesis Using Paired and Unpaired Data

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2361 ◽  
Author(s):  
Cheng-Bin Jin ◽  
Hakil Kim ◽  
Mingjie Liu ◽  
Wonmo Jung ◽  
Seongsu Joo ◽  
...  
Keyword(s):  
Screening Method ◽  
Training Data ◽  
Synthetic Methods ◽  
Training Methods ◽  
Ct Image ◽  
Mr Image ◽  
Generative Adversarial Network ◽  
Cardiac Pacemakers ◽  
Metal Implants ◽  
Adversarial Network

Magnetic resonance (MR) imaging plays a highly important role in radiotherapy treatment planning for the segmentation of tumor volumes and organs. However, the use of MR is limited, owing to its high cost and the increased use of metal implants for patients. This study is aimed towards patients who are contraindicated owing to claustrophobia and cardiac pacemakers, and many scenarios in which only computed tomography (CT) images are available, such as emergencies, situations lacking an MR scanner, and situations in which the cost of obtaining an MR scan is prohibitive. From medical practice, our approach can be adopted as a screening method by radiologists to observe abnormal anatomical lesions in certain diseases that are difficult to diagnose by CT. The proposed approach can estimate an MR image based on a CT image using paired and unpaired training data. In contrast to existing synthetic methods for medical imaging, which depend on sparse pairwise-aligned data or plentiful unpaired data, the proposed approach alleviates the rigid registration of paired training, and overcomes the context-misalignment problem of unpaired training. A generative adversarial network was trained to transform two-dimensional (2D) brain CT image slices into 2D brain MR image slices, combining the adversarial, dual cycle-consistent, and voxel-wise losses. Qualitative and quantitative comparisons against independent paired and unpaired training methods demonstrated the superiority of our approach.

scholarly journals Visual and Quantitative Evaluation of Amyloid Brain PET Image Synthesis with Generative Adversarial Network

2020 ◽  
Vol 10 (7) ◽  
pp. 2628 ◽  
Author(s):  
Hyeon Kang ◽  
Jang-Sik Park ◽  
Kook Cho ◽  
Do-Young Kang
Keyword(s):  
Quantitative Evaluation ◽  
Data Augmentation ◽  
Image Synthesis ◽  
Turing Test ◽  
Training Data ◽  
Generative Adversarial Network ◽  
Visual Evaluation ◽  
Image Distribution ◽  
Adversarial Network ◽  
Quantitative Measurements

Conventional data augmentation (DA) techniques, which have been used to improve the performance of predictive models with a lack of balanced training data sets, entail an effort to define the proper repeating operation (e.g., rotation and mirroring) according to the target class distribution. Although DA using generative adversarial network (GAN) has the potential to overcome the disadvantages of conventional DA, there are not enough cases where this technique has been applied to medical images, and in particular, not enough cases where quantitative evaluation was used to determine whether the generated images had enough realism and diversity to be used for DA. In this study, we synthesized 18F-Florbetaben (FBB) images using CGAN. The generated images were evaluated using various measures, and we presented the state of the images and the similarity value of quantitative measurement that can be expected to successfully augment data from generated images for DA. The method includes (1) conditional WGAN-GP to learn the axial image distribution extracted from pre-processed 3D FBB images, (2) pre-trained DenseNet121 and model-agnostic metrics for visual and quantitative measurements of generated image distribution, and (3) a machine learning model for observing improvement in generalization performance by generated dataset. The Visual Turing test showed similarity in the descriptions of typical patterns of amyloid deposition for each of the generated images. However, differences in similarity and classification performance per axial level were observed, which did not agree with the visual evaluation. Experimental results demonstrated that quantitative measurements were able to detect the similarity between two distributions and observe mode collapse better than the Visual Turing test and t-SNE.

Fine-Grained Semantic Image Synthesis with Object-Attention Generative Adversarial Network

2021 ◽  
Vol 12 (5) ◽  
pp. 1-18
Author(s):  
Min Wang ◽  
Congyan Lang ◽  
Liqian Liang ◽  
Songhe Feng ◽  
Tao Wang ◽  
...  
Keyword(s):  
Feature Fusion ◽  
Image Synthesis ◽  
Image Features ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Fine Grained ◽  
Multi Scale ◽  
Adversarial Network ◽  
Object Attention ◽  
Object Features

Semantic image synthesis is a new rising and challenging vision problem accompanied by the recent promising advances in generative adversarial networks. The existing semantic image synthesis methods only consider the global information provided by the semantic segmentation mask, such as class label, global layout, and location, so the generative models cannot capture the rich local fine-grained information of the images (e.g., object structure, contour, and texture). To address this issue, we adopt a multi-scale feature fusion algorithm to refine the generated images by learning the fine-grained information of the local objects. We propose OA-GAN, a novel object-attention generative adversarial network that allows attention-driven, multi-fusion refinement for fine-grained semantic image synthesis. Specifically, the proposed model first generates multi-scale global image features and local object features, respectively, then the local object features are fused into the global image features to improve the correlation between the local and the global. In the process of feature fusion, the global image features and the local object features are fused through the channel-spatial-wise fusion block to learn ‘what’ and ‘where’ to attend in the channel and spatial axes, respectively. The fused features are used to construct correlation filters to obtain feature response maps to determine the locations, contours, and textures of the objects. Extensive quantitative and qualitative experiments on COCO-Stuff, ADE20K and Cityscapes datasets demonstrate that our OA-GAN significantly outperforms the state-of-the-art methods.

scholarly journals Adversarial Learning for Product Recommendation

AI ◽  
2020 ◽  
Vol 1 (3) ◽  
pp. 376-388
Author(s):  
Joel R. Bock ◽  
Akhilesh Maewal
Keyword(s):  
Joint Distribution ◽  
Large Scale ◽  
User Interaction ◽  
Product Category ◽  
Training Data ◽  
Generative Adversarial Network ◽  
Product Recommendation ◽  
Adversarial Network ◽  
Null Hypothesis Testing ◽  
Conversion Rates

Product recommendation can be considered as a problem in data fusion—estimation of the joint distribution between individuals, their behaviors, and goods or services of interest. This work proposes a conditional, coupled generative adversarial network (RecommenderGAN) that learns to produce samples from a joint distribution between (view, buy) behaviors found in extremely sparse implicit feedback training data. User interaction is represented by two matrices having binary-valued elements. In each matrix, nonzero values indicate whether a user viewed or bought a specific item in a given product category, respectively. By encoding actions in this manner, the model is able to represent entire, large scale product catalogs. Conversion rate statistics computed on trained GAN output samples ranged from 1.323% to 1.763%. These statistics are found to be significant in comparison to null hypothesis testing results. The results are shown comparable to published conversion rates aggregated across many industries and product types. Our results are preliminary, however they suggest that the recommendations produced by the model may provide utility for consumers and digital retailers.

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.

Role of General Adversarial Networks in Mammogram Analysis: A Review

2020 ◽  
Vol 16 (7) ◽  
pp. 863-877
Author(s):  
Annapoorani Gopal ◽  
Lathaselvi Gandhimaruthian ◽  
Javid Ali
Keyword(s):  
Breast Tumor ◽  
Deep Neural Networks ◽  
Training Data ◽  
Learning Technology ◽  
Breast Cancers ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Tumor Extraction

The Deep Neural Networks have gained prominence in the biomedical domain, becoming the most commonly used networks after machine learning technology. Mammograms can be used to detect breast cancers with high precision with the help of Convolutional Neural Network (CNN) which is deep learning technology. An exhaustive labeled data is required to train the CNN from scratch. This can be overcome by deploying Generative Adversarial Network (GAN) which comparatively needs lesser training data during a mammogram screening. In the proposed study, the application of GANs in estimating breast density, high-resolution mammogram synthesis for clustered microcalcification analysis, effective segmentation of breast tumor, analysis of the shape of breast tumor, extraction of features and augmentation of the image during mammogram classification have been extensively reviewed.

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