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Segmentation of Lungs COVID Infected Regions by Attention Mechanism and Synthetic Data [article]

Parham Yazdekhasty, Ali Zindari, Zahra Nabizadeh-ShahreBabak, Pejman Khadivi, Nader Karimi, Shadrokh Samavi
2021 arXiv   pre-print
This research proposes a method for segmenting infected lung regions in a CT image.  ...  Attention blocks improve the segmentation accuracy by focusing on informative parts of the image.  ...  This dataset contains 20-labeled COVID-19 CT scans. The left lung, right lung, and infected regions are labeled by a radiologist and verified by another experienced radiologist.  ... 
arXiv:2108.08895v2 fatcat:emxiw3g5sjf73fs6yovflzkvs4

COVID-Rate: An Automated Framework for Segmentation of COVID-19 Lesions from Chest CT Scans [article]

Nastaran Enshaei, Anastasia Oikonomou, Moezedin Javad Rafiee, Parnian Afshar, Shahin Heidarian, Arash Mohammadi, Konstantinos N. Plataniotis, Farnoosh Naderkhani
2021 arXiv   pre-print
During pandemic era, however, visual assessment and quantification of COVID-19 lung lesions by expert radiologists become expensive and prone to error, which raises an urgent quest to develop practical  ...  Furthermore, the results indicate that the COVID-Rate model can efficiently segment COVID-19 lesions in both 2D CT images and whole lung volumes.  ...  Data/Code Availability The datasets/codes generated and/or analyzed during the current study will be released publicly upon potential publication of the study.  ... 
arXiv:2107.01527v1 fatcat:tnoki62cqbfxvoi2kvf7tom5m4

COVID-19 CT Image Synthesis with a Conditional Generative Adversarial Network

Yifan Jiang, Han Chen, M. H. Loew, Hanseok Ko
2020 IEEE journal of biomedical and health informatics  
However, training a deep-learning model requires large volumes of data, and medical staff faces a high risk when collecting COVID-19 CT data due to the high infectivity of the disease.  ...  Chest CT imaging offers the benefits of quick reporting, a low cost, and high sensitivity for the detection of pulmonary infection.  ...  By replacing the real data with a ratio of synthetic data, the semantic segmentation performance of Unet does not decrease and stay at a stable level.  ... 
doi:10.1109/jbhi.2020.3042523 pmid:33275588 fatcat:k4mwfnoj3zgm3jrvccn2iianv4

COVID-19 CT Image Synthesis with a Conditional Generative Adversarial Network [article]

Yifan Jiang, Han Chen, Murray Loew, Hanseok Ko
2020 arXiv   pre-print
However, training a deep-learning model requires large volumes of data, and medical staff faces a high risk when collecting COVID-19 CT data due to the high infectivity of the disease.  ...  Chest CT imaging offers the benefits of quick reporting, a low cost, and high sensitivity for the detection of pulmonary infection.  ...  By replacing the real data with a ratio of synthetic data, the semantic segmentation performance of Unet does not decrease and stay at a stable level.  ... 
arXiv:2007.14638v2 fatcat:nwaquofzrjeodnuufqjqjbbtpu

COVID-19 Detection based on Image Regrouping and ResNet-SVM using Chest X-ray Images

Changjian Zhou, Jia Song, Sihan Zhou, Zhiyao Zhang, Jinge Xing
2021 IEEE Access  
The lung region was segmented from the original chest X-ray images and divided into small pieces, and then the small pieces of lung region were regrouped into a regular image randomly.  ...  By analyzing the COVID-19 chest X-ray images, a combination method of image regrouping and ResNet-SVM was proposed in this study.  ...  ACKNOWLEDGMENT The authors would like to thank the High Performance Computing platform of Northeast Agricultural University for providing computing resources and technical support.  ... 
doi:10.1109/access.2021.3086229 pmid:34812395 pmcid:PMC8545189 fatcat:m4akwwvivzf43mchuzrxaimety

Domain Adaptation based COVID-19 CT Lung Infections Segmentation Network [article]

Han Chen and Yifan Jiang and Hanseok Ko
2020 arXiv   pre-print
This makes the embedding distribution learned by segmentation network from real data and synthetic data closer, thus greatly improving the representation ability of the segmentation network.  ...  In order to solve this issue, we propose a novel domain adaptation based COVID-19 CT lung infections segmentation network.  ...  [18] developed a 3D CNN network for COVID-19 infection segmentation, and proposed a dual-sampling attention mechanism to alleviate the imbalanced problem of data. Oulefki et al.  ... 
arXiv:2011.11242v1 fatcat:nyu7cz2ccjd3znlz2ftbejapwi

Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review [article]

Felipe Giuste, Wenqi Shi, Yuanda Zhu, Tarun Naren, Monica Isgut, Ying Sha, Li Tong, Mitali Gupte, May D. Wang
2021 arXiv   pre-print
The impact of artificial intelligence during the COVID-19 pandemic was greatly limited by lack of model transparency.  ...  Finally, we provide a checklist of suggestions during the experimental design process supported by recent publications.  ...  This research was supported by a Wallace H. Coulter Distinguished Faculty Fellowship (M. D. Wang), a Petit Institute Faculty Fellowship (M. D. Wang), and by Microsoft Research.  ... 
arXiv:2112.12705v2 fatcat:pji2saeikbeq7phmygphbomm5e

Attention Based Residual Network for Effective Detection of COVID-19 and Viral Pneumonia

Muhammad Aasharib Nawshad, Usama Aleem Shami, Sana Sajid, Muhammad Moazam Fraz
2021 2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2)  
these segmented images into IAVP, ITI (irrelevant to infection), and COVID classes.  ...  The first step in their research was to use a deep learning model to identify candidate infected regions and segment them from pulmonary CT samples, followed by a location-oriented attention model to classify  ... 
doi:10.1109/icodt252288.2021.9441485 fatcat:enehkor2ynhsdn7m5qme5e64o4

CCS-GAN: COVID-19 CT-scan classification with very few positive training images [article]

Sumeet Menon, Jayalakshmi Mangalagiri, Josh Galita, Michael Morris, Babak Saboury, Yaacov Yesha, Yelena Yesha, Phuong Nguyen, Aryya Gangopadhyay, David Chapman
2021 arXiv   pre-print
CCS-GAN combines style transfer with pulmonary segmentation and relevant transfer learning from negative images in order to create a larger volume of synthetic positive images for the purposes of improving  ...  CCS-GAN achieves high accuracy with few positive images and thereby greatly reduces the barrier of acquiring large training volumes in order to train a diagnostic classifier for COVID-19.  ...  Li et al [19] extended these approaches by combining GANs with ensemble learning and attention mechanisms.  ... 
arXiv:2110.01605v1 fatcat:254jjytfzjb4jd5p7oydvp6ivi

Convolutional neural networks for the diagnosis and prognosis of the coronavirus disease pandemic

Sneha Kugunavar, C J Prabhakar
2021 Visual Computing for Industry, Biomedicine, and Art  
The CNN models discussed in this review were mainly developed for the detection, classification, and segmentation of COVID-19 images.  ...  In this article, we present the application of CNNs for the diagnosis and prognosis of COVID-19 using X-ray and computed tomography (CT) images of COVID-19 patients.  ...  [60] developed a COVID-19 lung infection segmentation deep network called Inf-Net to automatically delineate the infected region from CT images.  ... 
doi:10.1186/s42492-021-00078-w pmid:33950399 fatcat:msd5gal6vvdqzhasixgxd2pd5a

Machine learning models for image-based diagnosis and prognosis of COVID-19: A systematic review (Preprint)

Mahdieh Montazeri, Roxana ZahediNasab, Ali Farahani, Hadis Mohseni, Fahimeh Ghasemian
2020 JMIR Medical Informatics  
age, CT data, gender, comorbidities, symptoms and laboratory findings.  ...  Machine learning methods can play vital roles in diagnosing COVID-19 by processing chest x-ray images.  ...  images from the Classification and segmentation Lung Infection Segmentation Deep Network (Inf-Net) is proposed to automatically identify infected regions from chest CT slices + semi- supervised  ... 
doi:10.2196/25181 pmid:33735095 fatcat:jdztginrrbap3bywkgm75bwt2m

A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicron [article]

Asifullah Khan, Saddam Hussain Khan, Mahrukh Saif, Asiya Batool, Anabia Sohail, Muhammad Waleed Khan
2022 arXiv   pre-print
DL techniques are systematically categorized into classification, segmentation, and multi-stage approaches for COVID-19 diagnosis at image and region level analysis.  ...  effectively dealing with new variants of COVID-19 and emerging challenges.  ...  the infected region of the lungs 105 .  ... 
arXiv:2202.06372v2 fatcat:cakw34chu5dbxpt4kp67ibukl4

Front Matter: Volume 11597

Karen Drukker, Maciej A. Mazurowski
2021 Medical Imaging 2021: Computer-Aided Diagnosis  
by adversarial U-Net [11597-42] 11597 1A Dense-layer-based YOLO-v3 for detection and localization of colon perforations [11597-43] 11597 1B Deep attention mask regional convolutional neural network  ...  concrete autoencoder for COVID-19 lung CT images 11597 0V Detecting COVID-19 infected pneumonia from x-ray images using a deep learning model with image preprocessing algorithm BREAST II 0W An improved  ...  CT images 11597 2V How to select training data to segment mammary gland region using a deep-learning approach for reliable individualized screening mammography viii Proc. of SPIE Vol. 11597 1159701-  ... 
doi:10.1117/12.2595447 fatcat:u25cvo7adbgcxb363rsnsgnsju

End-to-end learning for semiquantitative rating of COVID-19 severity on Chest X-rays [article]

Alberto Signoroni, Mattia Savardi, Sergio Benini, Nicola Adami, Riccardo Leonardi, Paolo Gibellini, Filippo Vaccher, Marco Ravanelli, Andrea Borghesi, Roberto Maroldi, Davide Farina
2020 arXiv   pre-print
In this work we design an end-to-end deep learning architecture for predicting, on Chest X-rays images (CXR), a multi-regional score conveying the degree of lung compromise in COVID-19 patients.  ...  Our BS-Net demonstrates self-attentive behavior and a high degree of accuracy in all processing stages.  ...  A very special thanks to Marco Renato Roberto Merli, and Andrea Carola of Philips S.p.A., and to Gian Stefano Bosio, and Simone Gaggero of EL.CO.  ... 
arXiv:2006.04603v2 fatcat:3lcnevkgojaqlclj7xnc6adrhm

Let AI Perform Better Next Time—A Systematic Review of Medical Imaging-Based Automated Diagnosis of COVID-19: 2020–2022

Fan Liu, Delong Chen, Xiaocong Zhou, Wenwen Dai, Feng Xu
2022 Applied Sciences  
The pandemic of COVID-19 has caused millions of infections, which has led to a great loss all over the world, socially and economically.  ...  ., classification-based methods) and pixel-level diagnosis (i.e., segmentation-based models).  ...  Lung region segmentation is used to separate the whole lung region from the background, and lung lesion segmentation is used to distinguish the lesion region from other lung regions.  ... 
doi:10.3390/app12083895 fatcat:ysfbwmbzijc67ooz66265fkuna
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