Generalization of Deep Neural Networks for Chest Pathology Classification in X-Rays Using Generative Adversarial Networks

Salehinejad, Hojjat; Valaee, Shahrokh; Dowdell, Tim; Colak, Errol; Barfett, Joseph

doi:10.48550/arxiv.1712.01636

Cited by 5 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this model, a novel proximal learning framework is developed, which adopts ResNets to model the proximals and a mixture of pixel-wise and perceptual costs are used for training. The deep convolutional generative adversarial networks are developed in [124] to generate artificial chest radiographs for automated abnormality detection in chest radiographs. This model can be extended to medical image modalities which have spatial and temporal dependencies, such as head magnetic resonance imaging (MRI), using RCNNs.…”

Section: Imagementioning

confidence: 99%

Recent Advances in Recurrent Neural Networks

Salehinejad,

Sankar,

Barfett

et al. 2018

Preprint

Self Cite

139

118

View full text Add to dashboard Cite

Recurrent neural networks (RNNs) are capable of learning features and long term dependencies from sequential and time-series data. The RNNs have a stack of non-linear units where at least one connection between units forms a directed cycle. A well-trained RNN can model any dynamical system; however, training RNNs is mostly plagued by issues in learning long-term dependencies. In this paper, we present a survey on RNNs and several new advances for newcomers and professionals in the field. The fundamentals and recent advances are explained and the research challenges are introduced.

show abstract

Section: Imagementioning

confidence: 99%

Recent Advances in Recurrent Neural Networks

Salehinejad,

Sankar,

Barfett

et al. 2018

Preprint

Self Cite

139

118

View full text Add to dashboard Cite

show abstract

“…GANs to generate and analyze data on the activity patterns of neurons, with the goal of creating data exhibiting the same statistics as the real data. To mitigate imbalanced data, [Salehinejad et al 2017] used GANs to generate synthetic chest x-ray images. The synthetic data augmented samples of rare conditions and training with the generated data was…”

Section: Gans For Synthetic Data Generation Embedding and Anomaly Det...mentioning

confidence: 99%

Coupled IGMM-GANs with Applications to Anomaly Detection in Human Mobility Data

Smolyak

Gray

Badirli

et al. 2020

ACM Trans. Spatial Algorithms Syst.

View full text Add to dashboard Cite

Detecting anomalous activity in human mobility data has a number of applications, including road hazard sensing, telematics-based insurance, and fraud detection in taxi services and ride sharing. In this article, we address two challenges that arise in the study of anomalous human trajectories: (1) a lack of ground truth data on what defines an anomaly and (2) the dependence of existing methods on significant pre-processing and feature engineering. Although generative adversarial networks (GANs) seem like a natural fit for addressing these challenges, we find that existing GAN-based anomaly detection algorithms perform poorly due to their inability to handle multimodal patterns. For this purpose, we introduce an infinite Gaussian mixture model coupled with (bidirectional) GANs—IGMM-GAN—that is able to generate synthetic, yet realistic, human mobility data and simultaneously facilitates multimodal anomaly detection. Through the estimation of a generative probability density on the space of human trajectories, we are able to generate realistic synthetic datasets that can be used to benchmark existing anomaly detection methods. The estimated multimodal density also allows for a natural definition of outlier that we use for detecting anomalous trajectories. We illustrate our methodology and its improvement over existing GAN anomaly detection on several human mobility datasets, along with MNIST.

show abstract

“…Chuquicusma et al [5] proposed to use unsupervised learning with Deep Convolutional Generative Adversarial Networks (DC-GANs) to generate realistic lung nodule samples and evaluated the quality of generated nodules by presenting Visual Turing tests to two radiologists. Salehinejad et al [6] demonstrated that augmenting the original imbalanced dataset with DCGAN generated images improved chest pathology classification performance. Madani et al [7] utilized deep convolutional generative adversarial networks in a semi-supervised learning architecture for classification of cardiac abnormality in chest X-rays.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Deep Convolutional Generative Adversarial Networks for Data Augmentation of Chest X-ray Images

Venu

Ravula

2020

Future Internet

View full text Add to dashboard Cite

Medical image datasets are usually imbalanced due to the high costs of obtaining the data and time-consuming annotations. Training a deep neural network model on such datasets to accurately classify the medical condition does not yield the desired results as they often over-fit the majority class samples’ data. Data augmentation is often performed on the training data to address the issue by position augmentation techniques such as scaling, cropping, flipping, padding, rotation, translation, affine transformation, and color augmentation techniques such as brightness, contrast, saturation, and hue to increase the dataset sizes. Radiologists generally use chest X-rays for the diagnosis of pneumonia. Due to patient privacy concerns, access to such data is often protected. In this study, we performed data augmentation on the Chest X-ray dataset to generate artificial chest X-ray images of the under-represented class through generative modeling techniques such as the Deep Convolutional Generative Adversarial Network (DCGAN). With just 1341 chest X-ray images labeled as Normal, artificial samples were created by retaining similar characteristics to the original data with this technique. Evaluating the model resulted in a Fréchet Distance of Inception (FID) score of 1.289. We further show the superior performance of a CNN classifier trained on the DCGAN augmented dataset.

show abstract

Generalization of Deep Neural Networks for Chest Pathology Classification in X-Rays Using Generative Adversarial Networks

Cited by 5 publications

References 0 publications

Recent Advances in Recurrent Neural Networks

Recent Advances in Recurrent Neural Networks

Coupled IGMM-GANs with Applications to Anomaly Detection in Human Mobility Data

Evaluation of Deep Convolutional Generative Adversarial Networks for Data Augmentation of Chest X-ray Images

Contact Info

Product

Resources

About