Filters








328 Hits in 9.8 sec

Abnormal Chest X-ray Identification With Generative Adversarial One-Class Classifier [article]

Yuxing Tang and Youbao Tang and Mei Han and Jing Xiao and Ronald M. Summers
2019 arXiv   pre-print
In this paper, we propose an end-to-end architecture for abnormal chest X-ray identification using generative adversarial one-class learning.  ...  It thus enables distinguishing abnormal chest X-rays from normal ones.  ...  The authors thank NVIDIA for GPU donation.  ... 
arXiv:1903.02040v1 fatcat:fwlvd6hrqfg43mvfh2p4aznqgy

Anatomy-aware Self-supervised Learning for Anomaly Detection in Chest Radiographs [article]

Junya Sato, Yuki Suzuki, Tomohiro Wataya, Daiki Nishigaki, Kosuke Kita, Kazuki Yamagata, Noriyuki Tomiyama, Shoji Kido
2022 arXiv   pre-print
AnatPaste employs a threshold-based lung segmentation pretext task to create anomalies in normal chest radiographs, which are used for model pretraining.  ...  These anomalies are similar to real anomalies and help the model recognize them. We evaluate our model on three opensource chest radiograph datasets.  ...  It consists of 9,790 normal and 19,894 abnormal (labeled as no lung opacity/not normal or lung opacity) chest radiographs.  ... 
arXiv:2205.04282v2 fatcat:wve77c7rufde5hytzbgdnndbzq

Generative Adversarial Network Based Synthetic Learning and a Novel Domain Relevant Loss Term for Spine Radiographs [article]

Ethan Schonfeld, Anand Veeravagu
2022 arXiv   pre-print
Approach: A series of GANs were trained and applied for a downstream computer vision spine radiograph abnormality classification task.  ...  Problem: There is a lack of big data for the training of deep learning models in medicine, characterized by the time cost of data collection and privacy concerns.  ...  StyleGAN2-ADA was made to be a conditional GAN for 2 classes: normal spine radiographs (2303 true images) and abnormal spine radiographs (1470 true images).  ... 
arXiv:2205.02843v1 fatcat:awbgpo5yojf5jivkt5mc6cww4y

TUNA-Net: Task-oriented UNsupervised Adversarial Network for Disease Recognition in Cross-Domain Chest X-rays [article]

Yuxing Tang, Youbao Tang, Veit Sandfort, Jing Xiao, Ronald M. Summers
2019 arXiv   pre-print
The TUNA-Net framework is general and can be readily adapted to other learning tasks. We evaluate the proposed framework on two public chest X-ray datasets for pneumonia recognition.  ...  Notably, TUNA-Net achieves an AUC of 96.3% for pediatric pneumonia classification, which is very close to that of the supervised approach (98.1%), but without the need for labels on the target domain.  ...  The authors thank NVIDIA for GPU donations.  ... 
arXiv:1908.07926v1 fatcat:h2frvrescffr7m772dxjnhckom

Generative Adversarial Networks Improve the Reproducibility and Discriminative Power of Radiomic Features

Sandra Marcadent, Jeremy Hofmeister, Maria Giulia Preti, Steve P. Martin, Dimitri Van De Ville, Xavier Montet
2020 Radiology: Artificial Intelligence  
This deep learning approach may help counteract the sensitivity of RFs to differences in acquisition.Supplemental material is available for this article.© RSNA, 2020See also the commentary by Alderson  ...  Both ML classifiers and radiologists had difficulty recognizing the chest radiographs' manufacturer.  ...  Acknowledgments: We would like to thank Michel Kocher, PhD, and Simon Burgermeister, MSc, for their helpful comments about this work.  ... 
doi:10.1148/ryai.2020190035 pmid:33937823 pmcid:PMC8082326 fatcat:3byt4xqhinhilch6kdmhnm4lga

DL-CRC: Deep Learning-Based Chest Radiograph Classification for COVID-19 Detection: A Novel Approach

Sadman Sakib, Tahrat Tazrin, Mostafa M. Fouda, Zubair Md. Fadlullah, Mohsen Guizani
2020 IEEE Access  
Therefore, toward automating the COVID-19 detection, in this paper, we propose a viable and efficient deep learning-based chest radiograph classification (DL-CRC) framework to distinguish the COVID-19  ...  cases with high accuracy from other abnormal (e.g., pneumonia) and normal cases.  ...  Since the problem can be regarded as a classification task of normal, COVID-19, and other abnormal cases (e.g., pneumonia), we investigate the contemporary deep learning architectures suited for classification  ... 
doi:10.1109/access.2020.3025010 pmid:34976555 pmcid:PMC8675549 fatcat:g2rvvk6mmfc6jjdbsc3gpnjxy4

Anomaly Detection Approach to Identify Early Cases in a Pandemic using Chest X-rays [article]

Shehroz S. Khan, Faraz Khoshbakhtian, Ahmed Bilal Ashraf
2021 arXiv   pre-print
To solve this problem, we present several unsupervised deep learning approaches, including convolutional and adversarially trained autoencoder.  ...  We tested two settings on a publicly available dataset (COVIDx)by training the model on chest X-rays from (i) only healthy adults, and (ii) healthy and other non-COVID-19 pneumonia, and detected COVID-  ...  Unsupervised Deep Learning based Anomaly Detection For detecting anomalies in CXR images, we investigate two types of unsupervised deep learning approaches: convolutional autoencoder (CAE) 1 and adversarially  ... 
arXiv:2010.02814v2 fatcat:vwumcbf7qjf2xjva34stoeprlm

Anomaly Detection Approach to Identify Early Cases in a Pandemic using Chest X-rays

Shehroz S. Khan, Faraz Khoshbakhtian, Ahmed Bilal Ashraf
2021 Proceedings of the Canadian Conference on Artificial Intelligence  
To solve this problem, we present several unsupervised deep learning approaches, including convolutional and adversarially trained autoencoder.  ...  We tested two settings on a publicly available dataset (COVIDx) by training the model on chest X-rays from (i) only healthy adults, and (ii) healthy and other non-COVID-19 pneumonia, and detected COVID  ...  Unsupervised Deep Learning based Anomaly Detection For detecting anomalies in CXR images, we investigate two types of unsupervised deep learning approaches: convolutional autoencoder (CAE) 1 and adversarially  ... 
doi:10.21428/594757db.fab70f8a fatcat:huceiiodbfexrint663kx7qmne

Robust Classification from Noisy Labels: Integrating Additional Knowledge for Chest Radiography Abnormality Assessment [article]

Sebastian Gündel, Arnaud A. A. Setio, Florin C. Ghesu, Sasa Grbic, Bogdan Georgescu, Andreas Maier, Dorin Comaniciu
2021 arXiv   pre-print
Chest radiography is the most common radiographic examination performed in daily clinical practice for the detection of various heart and lung abnormalities.  ...  Experiments were performed on an extensive collection of 297,541 chest radiographs from 86,876 patients, leading to a state-of-the-art performance level for 17 abnormalities from 2 datasets.  ...  Disclaimer: The concepts and information presented in this paper are based on research results that are not commercially available.  ... 
arXiv:2104.05261v3 fatcat:qdspeqfhvbc7rgtwmbkzyv65vm

Systematic Study on Diagnosis of Lung Disorders using Machine Learning and Deep Learning Algorithms

R. Swathi Sri, A. Menaka Pushpa
2021 2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII)  
Deep learning branches out a scheme to cut and run of it. Deep learning models work on medical images to detect the type of lung disease.  ...  Presently, lung infection is severe to humans that leads to death if left untreated, and Tracking down a disease on the dot is a way we get out of a hock.  ...  normal COVID-19 and normal includes TB Abnormal that Normal, Yes -generative adversarial network (GAN)[3] model for data augmentation Yes No DL DL + ML ML DARI algorithm + CNN InceptionV3, Vgg19, DenseNet201  ... 
doi:10.1109/icbsii51839.2021.9445186 fatcat:wzitd6euyzg77efdfathektzli

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

Hojjat Salehinejad, Shahrokh Valaee, Tim Dowdell, Errol Colak, Joseph Barfett
2018 arXiv   pre-print
We employ a combination of real and artificial images to train a deep convolutional neural network (DCNN) to detect pathology across five classes of chest X-rays.  ...  Using chest X-rays as a model medical image, we implement a generative adversarial network (GAN) to create artificial images based upon a modest sized labeled dataset.  ...  In this manuscript, we propose the use of a deep convolutional generative adversarial network (DCGAN) for the generation of chest X-rays that mimic common chest pathologies.  ... 
arXiv:1712.01636v2 fatcat:bnnnbvrvhbgnjegrzc7shq47li

Computer-aided detection in chest radiography based on artificial intelligence: a survey

Chunli Qin, Demin Yao, Yonghong Shi, Zhijian Song
2018 BioMedical Engineering OnLine  
Availability of data and materials Not applicable. Consent for publication Not applicable. Ethics approval and consent to participate Not applicable.  ...  This research was also supported by grants from The National Key Research and Development Program of China (2016YFC0106102 and 2017YFC0110700).  ...  They used a Gabor filter and SVM to distinguish normal chest and pulmonary edema in chest radiographs, and they obtained an AUC of 0.96.  ... 
doi:10.1186/s12938-018-0544-y pmid:30134902 fatcat:moshts5kpjd4hpejcs2irwf6eq

Unsupervised Detection of Lung Nodules in Chest Radiography Using Generative Adversarial Networks [article]

Nitish Bhatt, David Ramon Prados, Nedim Hodzic, Christos Karanassios, H.R. Tizhoosh
2021 arXiv   pre-print
Lung nodules are commonly missed in chest radiographs. We propose and evaluate P-AnoGAN, an unsupervised anomaly detection approach for lung nodules in radiographs.  ...  External validation and testing are performed using healthy and unhealthy patches extracted from the ChestX-ray14 and Japanese Society for Radiological Technology datasets, respectively.  ...  normal class.  ... 
arXiv:2108.02233v1 fatcat:yrg2i644ffc6dcyxmmshgajr7m

Efficient GAN-based Chest Radiographs (CXR) augmentation to diagnose coronavirus disease pneumonia

Saleh Albahli
2020 International Journal of Medical Sciences  
Based on COVID-19 radiographical changes in CT images, this work aims to detect the possibility of COVID-19 in the patient.  ...  There is no effective method which can accurately identify all chest related diseases and tackle the multiple class problems with reliable results.  ...  Acknowledgment I would like to thank the Deanship of Scientific Research, Qassim University for funding publication of this project.  ... 
doi:10.7150/ijms.46684 pmid:32624700 pmcid:PMC7330663 fatcat:w4csq24pnnf2patdk4vec33ixe

Automatic Diagnosis of Pneumothorax from Chest Radiographs: A Systematic Literature Review [article]

Tahira Iqbal, Arslan Shaukat, Usman Akram, Zartasha Mustansar
2021 arXiv   pre-print
Lot of research has been done for automatic and fast detection of pneumothorax from chest radiographs while proposing several frameworks based on artificial intelligence and machine learning techniques  ...  Among various medical imaging tools, chest radiographs are the most important and widely used diagnostic tool for detection of thoracic pathologies.  ...  Funding No funding was received for this study.  ... 
arXiv:2012.11214v2 fatcat:c2oa374cvrgvteysabzokvn3ba
« Previous Showing results 1 — 15 out of 328 results