Self-supervised region-aware segmentation of COVID-19 CT images using 3D GAN and contrastive learning

Shabani, Siyavash; Homayounfar, Morteza; Vardhanabhuti, Varut; Mahani, Mohammad-Ali Nikouei; Koohi‐Moghadam, Mohamad

doi:10.1016/j.compbiomed.2022.106033

Cited by 14 publications

(7 citation statements)

References 24 publications

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“…Shabani et al [ 177 ] propose a novel strategy for COVID-19 segmentation using self-supervised learning. Segmenting medical images is an important first phase in many medical strategies.…”

Section: Deep Learning For Medical Image Analysis and Cadmentioning

confidence: 99%

On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

Iqbal

Qureshi

et al. 2023

Arch Computat Methods Eng

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Convolutional neural network (CNN) has shown dissuasive accomplishment on different areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information Retrieval, Medical Image Registration, Multi-lingual translation, Local language Processing, Anomaly Detection on video and Speech Recognition. CNN is a special type of Neural Network, which has compelling and effective learning ability to learn features at several steps during augmentation of the data. Recently, different interesting and inspiring ideas of Deep Learning (DL) such as different activation functions, hyperparameter optimization, regularization, momentum and loss functions has improved the performance, operation and execution of CNN Different internal architecture innovation of CNN and different representational style of CNN has significantly improved the performance. This survey focuses on internal taxonomy of deep learning, different models of vonvolutional neural network, especially depth and width of models and in addition CNN components, applications and current challenges of deep learning.

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“…Shabani et al [ 177 ] propose a novel strategy for COVID-19 segmentation using self-supervised learning. Segmenting medical images is an important first phase in many medical strategies.…”

Section: Deep Learning For Medical Image Analysis and Cadmentioning

confidence: 99%

On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

Iqbal

Qureshi

et al. 2023

Arch Computat Methods Eng

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“…SSL has been widely noticed by researchers in the areas of computer vision [32], natural language processing [33], etc. SSL mainly includes context-based [34], temporal-based [35], and comparative-based [36], which aim to learn a feature representation by constructing proxy labels without expensive manual labels. Recently, SSL has been adopted in fault diagnosis areas and has achieved satisfactory results.…”

Section: Self-supervised Learningmentioning

confidence: 99%

Prior knowledge-based self-supervised learning for intelligent bearing fault diagnosis with few fault samples

Wu,

Nie,

et al. 2023

Meas. Sci. Technol.

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Deep learning-based bearing fault diagnosis methods have been developed to learn fault knowledge from massive data. Owing to the deficiency of fault samples and the variability of working conditions, these deep-learning-based methods are limited in industrial applications. To address this problem, this study proposes a prior knowledge-based self-supervised learning (PKSSL) method for bearing fault diagnosis. In the PKSSL method, prior diagnostic knowledge is extracted by meta-learning from a few samples. Prior diagnostic knowledge is then utilized to guide the self-supervised learning (SSL) process to reduce reliance on training data. Furthermore, a graph convolutional network is introduced to fuse the information obtained by meta-learning and SSL, which makes the model fully utilize the learned information and improves the accuracy of the fault diagnosis. The effectiveness of the proposed method was validated using two datasets. The results demonstrate that, compared to other existing approaches, the proposed method exhibits a strong generalization ability to transfer diagnostic knowledge from artificial damage data to real damage data under varied operating conditions.

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“…Goodfellow, et al [12] combined two networks in adversarial states to create generative adversarial networks (GAN) for image and speech generation. Innovative networks based on GAN, such as the Progressive Growth Generative Adversarial Network (PGGAN) [13], and dual-model architectures using 3D GAN and contrast learning methods [14], have been experimented on the COVID-19 dataset for classification.…”

Section: Related Workmentioning

confidence: 99%

Gray Level Co-Occurrence Matrix and RVFL for Covid-19 Diagnosis

Tang

2023

EAI Endorsed Trans e-Learn

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As the widespread transmission of COVID-19 has continued to influence human health since late 2019, more intersections between artificial intelligence and the medical field have arisen. For CT images, manual differentiation between COVID-19-infected and healthy control images is not as effective and fast as AI. This study performed experiments on a dataset containing 640 samples, 320 of which were COVID-19-infected, and the rest were healthy controls. This experiment combines the gray-level co-occurrence matrix (GLCM) and random vector function link (RVFL). The role of GLCM and RVFL is to extract image features and classify images, respectively. The experimental results of my proposed GLCM-RVFL model are validated using K-fold cross-validation, and the indicators are 78.81±1.75%, 77.08±0.68%, 77.46±0.73%, 54.22±1.35%, and 77.48±0.74% for sensitivity, accuracy, F1-score, MCC, and FMI, respectively, which also confirms that the proposed model performs well on the COVID-19 detection task. After comparing with six state-of-the-art COVID-19 detection, I ensured that my model achieved higher performance.

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Self-supervised region-aware segmentation of COVID-19 CT images using 3D GAN and contrastive learning

Cited by 14 publications

References 24 publications

On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

Prior knowledge-based self-supervised learning for intelligent bearing fault diagnosis with few fault samples

Gray Level Co-Occurrence Matrix and RVFL for Covid-19 Diagnosis

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