Multilevel depth-wise context attention network with atrous mechanism for segmentation of COVID19 affected regions

Qayyum, Abdul; Mazhar, Mona; Razzak, Muhammad Imran; Bouadjenek, Mohamed Reda

doi:10.1007/s00521-021-06636-w

Cited by 13 publications

(12 citation statements)

References 31 publications

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“…It consisted of 224 total training patients and 101 total tests acquired from 5 different centers. A detailed description of the dataset can be found 36–38 …”

Section: Resultsmentioning

confidence: 99%

“…A detailed description of the dataset can be found. [36][37][38] II. Results: To estimate the number of survival days, we considered four previously mentioned feature extraction methods for the internal validation dataset: including clinical, CT/PET with radiomics, 3D deep segmentation-based features, and 3D regressor characteristics.…”

Section: Generalization Capability Analysismentioning

confidence: 99%

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Deep learning‐based survival prediction of brain tumor patients using attention‐guided 3D convolutional neural network with radiomics approach from multimodality magnetic resonance imaging

Mazher,

Qayyum,

Puig

et al. 2023

Int J Imaging Syst Tech

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Automatic survival prediction of gliomas from brain magnetic resonance imaging (MRI) volumes is an essential step for a patient's prognosis analysis. Radiomics research delivers beneficial feature information from MRI imaging which is substantially required by clinicians and oncologists for predicting disease prognosis for precise surgical treatment and planning. In recent years, the success of deep learning has been vast in the field of medical imaging, and it shows state‐of‐the‐art performance in applications like segmentation, classification, regression, and detection. Therefore, in this paper, we proposed a collective method using deep learning and radiomics techniques for the survival prediction of brain tumor patients. We first propose a hierarchical channel attention (HAM) module and a multi‐scale‐aware feature enhancement (MSAFE) to efficiently fuse adjacent hierarchical features in the proposed segmentation model. After segmentation, deep/latent features (LCNN) are extracted from the bottom layer of the proposed segmentation model. Later, we extracted selected radiomics features (histogram, location, and shape) using input images and segmented masks from the proposed segmentation model. Further, the 3D deep learning regressor has been trained for 3D regressor‐based deep feature extraction. We proposed the method of overall survival prediction for the brain tumor patients by combining all the meaningful features including clinical features (age) that also favorably contribute to the survival days prediction for the glioma's patients. To predict the survival days for each patient, the selected features are trained to analyze the performance of various regression techniques like random forest (RF), decision tree (DT), and XGBoost. Our proposed combined feature‐based method achieved the highest performance for survival days prediction over the state‐of‐the‐art methods. We also perform extensive experiments to show the effectiveness of each feature extraction method. The experimental results infer that deep learning‐based features along with radiomic features and clinical features are truly vital paradigms to estimate survival days.

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“…It consisted of 224 total training patients and 101 total tests acquired from 5 different centers. A detailed description of the dataset can be found 36–38 …”

Section: Resultsmentioning

confidence: 99%

Section: Generalization Capability Analysismentioning

confidence: 99%

Deep learning‐based survival prediction of brain tumor patients using attention‐guided 3D convolutional neural network with radiomics approach from multimodality magnetic resonance imaging

Mazher,

Qayyum,

Puig

et al. 2023

Int J Imaging Syst Tech

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“…For example, Chen [16] proposed a multi-scale attention-guided UNet that integrates local and global context information to enhance the segmentation of COVID-19 lesions. Qayyum [17] proposed a UNet-based model that incorporates spatial and channel-wise attention mechanisms to improve the performance of COVID-19 CT segmentation.…”

Section: Related Workmentioning

confidence: 99%

Comprehensive exploration of UNet adaptations for improved COVID-19 CT segmentation

2023

Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023)

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The COVID-19 pandemic has led to a widespread need for accurate and efficient medical image analysis, particularly in the context of lung CT scans. In this paper, we present a comprehensive exploration of UNet adaptations for COVID-19 CT segmentation, focusing on the impact of different loss functions and modifications to the UNet's Convolutional Neural Network (CNN) architecture. Our experimental results demonstrate the efficacy of these approaches in improving segmentation performance. We provide a thorough comparison of various loss functions and UNet modifications, highlighting the most effective combinations for robust and accurate COVID-19 CT segmentation.

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“…Actually, attention modules have showed their particular strengthen and values on detection of COVID-19. In [30], a context attention network, formed by 3D depth-wise and 3D residual squeezing and excitation block, was proposed for segmenting the lesion region on CT scans. Sitaula et al [31] utilized an attention module to enhance the spatial relationships between the regions of interests in CXR images.…”

Section: Recent Workmentioning

confidence: 99%

SC2Net: A Novel Segmentation-Based Classification Network for Detection of COVID-19 in Chest X-Ray Images

Zhao

Fang

Ren

et al. 2022

IEEE J. Biomed. Health Inform.

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The pandemic of COVID-19 has become a global crisis in public health, which has led to a massive number of deaths and severe economic degradation. To suppress the spread of COVID-19, accurate diagnosis at an early stage is crucial. As the popularly used real-time reverse transcriptase polymerase chain reaction (RT-PCR) swab test can be lengthy and inaccurate, chest screening with radiography imaging is still preferred. However, due to limited image data and the difficulty of the early-stage diagnosis, existing models suffer from ineffective feature extraction and poor network convergence and optimisation. To tackle these issues, a segmentation-based COVID-19 classification network, namely SC2Net, is proposed for effective detection of the COVID-19 from chest x-ray (CXR) images. The SC2Net consists of two subnets: a COVID-19 lung segmentation network (CLSeg), and a spatial attention network (SANet). In order to supress the interference from the background, the CLSeg is first applied to segment the lung region from the CXR. The segmented lung region is then fed to the SANet for classification and diagnosis of the COVID-19. As a shallow yet effective classifier, SANet takes the ResNet-18 as the feature extractor and enhances highlevel feature via the proposed spatial attention module. For performance evaluation, the COVIDGR 1.0 dataset is used, which is a high-quality dataset with various severity levels of the COVID-19. Experimental results have shown that, our SC2Net has an average accuracy of 84.23% and an average F1 score of 81.31% in detection of COVID-19, outperforming several state-of-the-art approaches.

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Multilevel depth-wise context attention network with atrous mechanism for segmentation of COVID19 affected regions

Cited by 13 publications

References 31 publications

Deep learning‐based survival prediction of brain tumor patients using attention‐guided 3D convolutional neural network with radiomics approach from multimodality magnetic resonance imaging

Deep learning‐based survival prediction of brain tumor patients using attention‐guided 3D convolutional neural network with radiomics approach from multimodality magnetic resonance imaging

Comprehensive exploration of UNet adaptations for improved COVID-19 CT segmentation

SC2Net: A Novel Segmentation-Based Classification Network for Detection of COVID-19 in Chest X-Ray Images

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