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Article ; Online: BoostCaps: A Boosted Capsule Network for Brain Tumor Classification.

Afshar, Parnian / Plataniotis, Konstantinos N / Mohammadi, Arash

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

2020 Volume 2020, Page(s) 1075–1079

Abstract: Brain tumor is among the deadliest cancers, whose effective treatment is partially dependent on the accurate diagnosis of the tumor type. Convolutional neural networks (CNNs), which have been the state-of-the-art in brain tumor classification, fail to ... ...

Abstract	Brain tumor is among the deadliest cancers, whose effective treatment is partially dependent on the accurate diagnosis of the tumor type. Convolutional neural networks (CNNs), which have been the state-of-the-art in brain tumor classification, fail to identify the spatial relations in the image. Capsule networks, proposed to overcome this drawback, are sensitive to miscellaneous backgrounds and cannot manage to focus on the main target. To address this shortcoming, we have recently proposed a capsule network-based architecture capable of taking both brain images and tumor rough boundary boxes as inputs, to have access to the surrounding tissue as well as the main target. Similar to other architectures, however, this network requires extensive search within the space of all possible configurations, to find the optimal architecture. To eliminate this need, in this study, we propose a boosted capsule network, referred to as BoostCaps, which takes advantage of the ability of boosting methods to handle weak learners, by gradually boosting the models. BoosCaps, to the best of our knowledge, is the first capsule network model that incorporates an internal boosting mechanism. Our results show that the proposed BoostCaps framework outperforms its single capsule network counterpart.
MeSH term(s)	Brain ; Brain Neoplasms ; Dietary Supplements ; Humans ; Neural Networks, Computer
Language	English
Publishing date	2020-10-05
Publishing country	United States
Document type	Journal Article
ISSN	2694-0604
ISSN (online)	2694-0604
DOI	10.1109/EMBC44109.2020.9175922
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: COVID-rate: an automated framework for segmentation of COVID-19 lesions from chest CT images.

Enshaei, Nastaran / Oikonomou, Anastasia / Rafiee, Moezedin Javad / Afshar, Parnian / Heidarian, Shahin / Mohammadi, Arash / Plataniotis, Konstantinos N / Naderkhani, Farnoosh

Scientific reports

2022 Volume 12, Issue 1, Page(s) 3212

Abstract: Novel Coronavirus disease (COVID-19) is a highly contagious respiratory infection that has had devastating effects on the world. Recently, new COVID-19 variants are emerging making the situation more challenging and threatening. Evaluation and ... ...

Abstract	Novel Coronavirus disease (COVID-19) is a highly contagious respiratory infection that has had devastating effects on the world. Recently, new COVID-19 variants are emerging making the situation more challenging and threatening. Evaluation and quantification of COVID-19 lung abnormalities based on chest Computed Tomography (CT) images can help determining the disease stage, efficiently allocating limited healthcare resources, and making informed treatment decisions. 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 autonomous solutions. In this context, first, the paper introduces an open-access COVID-19 CT segmentation dataset containing 433 CT images from 82 patients that have been annotated by an expert radiologist. Second, a Deep Neural Network (DNN)-based framework is proposed, referred to as the [Formula: see text], that autonomously segments lung abnormalities associated with COVID-19 from chest CT images. Performance of the proposed [Formula: see text] framework is evaluated through several experiments based on the introduced and external datasets. Third, an unsupervised enhancement approach is introduced that can reduce the gap between the training set and test set and improve the model generalization. The enhanced results show a dice score of 0.8069 and specificity and sensitivity of 0.9969 and 0.8354, respectively. Furthermore, the results indicate that the [Formula: see text] model can efficiently segment COVID-19 lesions in both 2D CT images and whole lung volumes. Results on the external dataset illustrate generalization capabilities of the [Formula: see text] model to CT images obtained from a different scanner.
MeSH term(s)	COVID-19/diagnostic imaging ; Datasets as Topic ; Female ; Humans ; Image Processing, Computer-Assisted/methods ; Male ; Middle Aged ; Neural Networks, Computer ; Radiography, Thoracic ; Tomography, X-Ray Computed
Language	English
Publishing date	2022-02-25
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Validation Study
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-022-06854-9
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: COVID-rate

Nastaran Enshaei / Anastasia Oikonomou / Moezedin Javad Rafiee / Parnian Afshar / Shahin Heidarian / Arash Mohammadi / Konstantinos N. Plataniotis / Farnoosh Naderkhani

Scientific Reports, Vol 12, Iss 1, Pp 1-

an automated framework for segmentation of COVID-19 lesions from chest CT images

2022 Volume 18

Abstract: Abstract Novel Coronavirus disease (COVID-19) is a highly contagious respiratory infection that has had devastating effects on the world. Recently, new COVID-19 variants are emerging making the situation more challenging and threatening. Evaluation and ... ...

Abstract	Abstract Novel Coronavirus disease (COVID-19) is a highly contagious respiratory infection that has had devastating effects on the world. Recently, new COVID-19 variants are emerging making the situation more challenging and threatening. Evaluation and quantification of COVID-19 lung abnormalities based on chest Computed Tomography (CT) images can help determining the disease stage, efficiently allocating limited healthcare resources, and making informed treatment decisions. 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 autonomous solutions. In this context, first, the paper introduces an open-access COVID-19 CT segmentation dataset containing 433 CT images from 82 patients that have been annotated by an expert radiologist. Second, a Deep Neural Network (DNN)-based framework is proposed, referred to as the $$\text {COVID-Rate}$$ COVID-Rate , that autonomously segments lung abnormalities associated with COVID-19 from chest CT images. Performance of the proposed $$\text {COVID-Rate}$$ COVID-Rate framework is evaluated through several experiments based on the introduced and external datasets. Third, an unsupervised enhancement approach is introduced that can reduce the gap between the training set and test set and improve the model generalization. The enhanced results show a dice score of 0.8069 and specificity and sensitivity of 0.9969 and 0.8354, respectively. Furthermore, the results indicate that the $$\text {COVID-Rate}$$ COVID-Rate model can efficiently segment COVID-19 lesions in both 2D CT images and whole lung volumes. Results on the external dataset illustrate generalization capabilities of the $$\text {COVID-Rate}$$ COVID-Rate model to CT images obtained from a different scanner.
Keywords	Medicine ; R ; Science ; Q
Subject code	006
Language	English
Publishing date	2022-02-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Robust framework for COVID-19 identication from a multicenter dataset of chest CT scans.

Khademi, Sadaf / Heidarian, Shahin / Afshar, Parnian / Enshaei, Nastaran / Naderkhani, Farnoosh / Rafiee, Moezedin Javad / Oikonomou, Anastasia / Shafiee, Akbar / Babaki Fard, Faranak / Plataniotis, Konstantinos N / Mohammadi, Arash

PloS one

2023 Volume 18, Issue 3, Page(s) e0282121

Abstract: The main objective of this study is to develop a robust deep learning-based framework to distinguish COVID-19, Community-Acquired Pneumonia (CAP), and Normal cases based on volumetric chest CT scans, which are acquired in different imaging centers using ... ...

Abstract	The main objective of this study is to develop a robust deep learning-based framework to distinguish COVID-19, Community-Acquired Pneumonia (CAP), and Normal cases based on volumetric chest CT scans, which are acquired in different imaging centers using different scanners and technical settings. We demonstrated that while our proposed model is trained on a relatively small dataset acquired from only one imaging center using a specific scanning protocol, it performs well on heterogeneous test sets obtained by multiple scanners using different technical parameters. We also showed that the model can be updated via an unsupervised approach to cope with the data shift between the train and test sets and enhance the robustness of the model upon receiving a new external dataset from a different center. More specifically, we extracted the subset of the test images for which the model generated a confident prediction and used the extracted subset along with the training set to retrain and update the benchmark model (the model trained on the initial train set). Finally, we adopted an ensemble architecture to aggregate the predictions from multiple versions of the model. For initial training and development purposes, an in-house dataset of 171 COVID-19, 60 CAP, and 76 Normal cases was used, which contained volumetric CT scans acquired from one imaging center using a single scanning protocol and standard radiation dose. To evaluate the model, we collected four different test sets retrospectively to investigate the effects of the shifts in the data characteristics on the model's performance. Among the test cases, there were CT scans with similar characteristics as the train set as well as noisy low-dose and ultra-low-dose CT scans. In addition, some test CT scans were obtained from patients with a history of cardiovascular diseases or surgeries. This dataset is referred to as the "SPGC-COVID" dataset. The entire test dataset used in this study contains 51 COVID-19, 28 CAP, and 51 Normal cases. Experimental results indicate that our proposed framework performs well on all test sets achieving total accuracy of 96.15% (95%CI: [91.25-98.74]), COVID-19 sensitivity of 96.08% (95%CI: [86.54-99.5]), CAP sensitivity of 92.86% (95%CI: [76.50-99.19]), Normal sensitivity of 98.04% (95%CI: [89.55-99.95]) while the confidence intervals are obtained using the significance level of 0.05. The obtained AUC values (One class vs Others) are 0.993 (95%CI: [0.977-1]), 0.989 (95%CI: [0.962-1]), and 0.990 (95%CI: [0.971-1]) for COVID-19, CAP, and Normal classes, respectively. The experimental results also demonstrate the capability of the proposed unsupervised enhancement approach in improving the performance and robustness of the model when being evaluated on varied external test sets.
MeSH term(s)	Humans ; COVID-19/diagnostic imaging ; Retrospective Studies ; Tomography, X-Ray Computed ; Cone-Beam Computed Tomography ; Benchmarking
Language	English
Publishing date	2023-03-02
Publishing country	United States
Document type	Multicenter Study ; Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0282121
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Robust framework for COVID-19 identication from a multicenter dataset of chest CT scans

Sadaf Khademi / Shahin Heidarian / Parnian Afshar / Nastaran Enshaei / Farnoosh Naderkhani / Moezedin Javad Rafiee / Anastasia Oikonomou / Akbar Shafiee / Faranak Babaki Fard / Konstantinos N. plataniotis / Arash Mohammadi

PLoS ONE, Vol 18, Iss

2023 Volume 3

Abstract	The main objective of this study is to develop a robust deep learning-based framework to distinguish COVID-19, Community-Acquired Pneumonia (CAP), and Normal cases based on volumetric chest CT scans, which are acquired in different imaging centers using different scanners and technical settings. We demonstrated that while our proposed model is trained on a relatively small dataset acquired from only one imaging center using a specific scanning protocol, it performs well on heterogeneous test sets obtained by multiple scanners using different technical parameters. We also showed that the model can be updated via an unsupervised approach to cope with the data shift between the train and test sets and enhance the robustness of the model upon receiving a new external dataset from a different center. More specifically, we extracted the subset of the test images for which the model generated a confident prediction and used the extracted subset along with the training set to retrain and update the benchmark model (the model trained on the initial train set). Finally, we adopted an ensemble architecture to aggregate the predictions from multiple versions of the model. For initial training and development purposes, an in-house dataset of 171 COVID-19, 60 CAP, and 76 Normal cases was used, which contained volumetric CT scans acquired from one imaging center using a single scanning protocol and standard radiation dose. To evaluate the model, we collected four different test sets retrospectively to investigate the effects of the shifts in the data characteristics on the model’s performance. Among the test cases, there were CT scans with similar characteristics as the train set as well as noisy low-dose and ultra-low-dose CT scans. In addition, some test CT scans were obtained from patients with a history of cardiovascular diseases or surgeries. This dataset is referred to as the “SPGC-COVID” dataset. The entire test dataset used in this study contains 51 COVID-19, 28 CAP, and 51 Normal cases. Experimental results indicate ...
Keywords	Medicine ; R ; Science ; Q
Subject code	310
Language	English
Publishing date	2023-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Robust framework for COVID-19 identication from a multicenter dataset of chest CT scans.

PLoS ONE, Vol 18, Iss 3, p e

2023 Volume 0282121

Abstract	The main objective of this study is to develop a robust deep learning-based framework to distinguish COVID-19, Community-Acquired Pneumonia (CAP), and Normal cases based on volumetric chest CT scans, which are acquired in different imaging centers using different scanners and technical settings. We demonstrated that while our proposed model is trained on a relatively small dataset acquired from only one imaging center using a specific scanning protocol, it performs well on heterogeneous test sets obtained by multiple scanners using different technical parameters. We also showed that the model can be updated via an unsupervised approach to cope with the data shift between the train and test sets and enhance the robustness of the model upon receiving a new external dataset from a different center. More specifically, we extracted the subset of the test images for which the model generated a confident prediction and used the extracted subset along with the training set to retrain and update the benchmark model (the model trained on the initial train set). Finally, we adopted an ensemble architecture to aggregate the predictions from multiple versions of the model. For initial training and development purposes, an in-house dataset of 171 COVID-19, 60 CAP, and 76 Normal cases was used, which contained volumetric CT scans acquired from one imaging center using a single scanning protocol and standard radiation dose. To evaluate the model, we collected four different test sets retrospectively to investigate the effects of the shifts in the data characteristics on the model's performance. Among the test cases, there were CT scans with similar characteristics as the train set as well as noisy low-dose and ultra-low-dose CT scans. In addition, some test CT scans were obtained from patients with a history of cardiovascular diseases or surgeries. This dataset is referred to as the "SPGC-COVID" dataset. The entire test dataset used in this study contains 51 COVID-19, 28 CAP, and 51 Normal cases. Experimental results indicate ...
Keywords	Medicine ; R ; Science ; Q
Subject code	310
Language	English
Publishing date	2023-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: COVID-CAPS: A capsule network-based framework for identification of COVID-19 cases from X-ray images.

Afshar, Parnian / Heidarian, Shahin / Naderkhani, Farnoosh / Oikonomou, Anastasia / Plataniotis, Konstantinos N / Mohammadi, Arash

Pattern recognition letters

2020 Volume 138, Page(s) 638–643

Abstract: Novel Coronavirus disease (COVID-19) has abruptly and undoubtedly changed the world as we know it at the end of the 2nd decade of the 21st century. COVID-19 is extremely contagious and quickly spreading globally making its early diagnosis of paramount ... ...

Abstract	Novel Coronavirus disease (COVID-19) has abruptly and undoubtedly changed the world as we know it at the end of the 2nd decade of the 21st century. COVID-19 is extremely contagious and quickly spreading globally making its early diagnosis of paramount importance. Early diagnosis of COVID-19 enables health care professionals and government authorities to break the chain of transition and flatten the epidemic curve. The common type of COVID-19 diagnosis test, however, requires specific equipment and has relatively low sensitivity. Computed tomography (CT) scans and X-ray images, on the other hand, reveal specific manifestations associated with this disease. Overlap with other lung infections makes human-centered diagnosis of COVID-19 challenging. Consequently, there has been an urgent surge of interest to develop Deep Neural Network (DNN)-based diagnosis solutions, mainly based on Convolutional Neural Networks (CNNs), to facilitate identification of positive COVID-19 cases. CNNs, however, are prone to lose spatial information between image instances and require large datasets. The paper presents an alternative modeling framework based on Capsule Networks, referred to as the COVID-CAPS, being capable of handling small datasets, which is of significant importance due to sudden and rapid emergence of COVID-19. Our results based on a dataset of X-ray images show that COVID-CAPS has advantage over previous CNN-based models. COVID-CAPS achieved an Accuracy of 95.7%, Sensitivity of 90%, Specificity of 95.8%, and Area Under the Curve (AUC) of 0.97, while having far less number of trainable parameters in comparison to its counterparts. To potentially and further improve diagnosis capabilities of the COVID-CAPS, pre-training and transfer learning are utilized based on a new dataset constructed from an external dataset of X-ray images. This is in contrary to existing works on COVID-19 detection where pre-training is performed based on natural images. Pre-training with a dataset of similar nature further improved accuracy to 98.3% and specificity to 98.6%.
Keywords	covid19
Language	English
Publishing date	2020-09-16
Publishing country	Netherlands
Document type	Journal Article ; Review
ISSN	0167-8655
ISSN	0167-8655
DOI	10.1016/j.patrec.2020.09.010
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: COVID-CAPS: A Capsule Network-based Framework for Identification of COVID-19 cases from X-ray Images

Parnian Afshar / Shahin Heidarian / Farnoosh Naderkhani / Anastasia Oikonomou / Konstantinos Plataniotis N. / Arash Mohammadi

Abstract	Novel Coronavirus disease (COVID-19) has abruptly and undoubtedly changed the world as we know it at the end of the 2nd decade of the 21st century. COVID-19 is extremely contagious and quickly spreading globally making its early diagnosis of paramount importance. Early diagnosis of COVID-19 enables health care professionals and government authorities to break the chain of transition and flatten the epidemic curve. The common type of COVID-19 diagnosis test, however, requires specific equipment and has relatively low sensitivity. Computed tomography (CT) scans and X-ray images, on the other hand, reveal specific manifestations associated with this disease. Overlap with other lung infections makes human-centered diagnosis of COVID-19 challenging. Consequently, there has been an urgent surge of interest to develop Deep Neural Network (DNN)-based diagnosis solutions, mainly based on Convolutional Neural Networks (CNNs), to facilitate identification of positive COVID-19 cases. CNNs, however, are prone to lose spatial information between image instances and require large datasets. The paper presents an alternative modeling framework based on Capsule Networks, referred to as the COVID-CAPS, being capable of handling small datasets, which is of significant importance due to sudden and rapid emergence of COVID-19. Our results based on a dataset of X-ray images show that COVID-CAPS has advantage over previous CNN-based models. COVID-CAPS achieved an Accuracy of 95.7%, Sensitivity of 90%, Specificity of 95.8%, and Area Under the Curve (AUC) of 0.97, while having far less number of trainable parameters in comparison to its counterparts. To further improve diagnosis capabilities of the COVID-CAPS, pre-training based on a new dataset constructed from an external dataset of X-ray images. Pre-training with a dataset of similar nature further improved accuracy to 98.3% and specificity to 98.6%.
Keywords	covid19
Publisher	arxiv
Document type	Article
Database	COVID19

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Article ; Online: COVID-CAPS

Afshar, Parnian / Heidarian, Shahin / Naderkhani, Farnoosh / Oikonomou, Anastasia / Plataniotis, Konstantinos N. / Mohammadi, Arash

Pattern Recognition Letters

A capsule network-based framework for identification of COVID-19 cases from X-ray images

2020 Volume 138, Page(s) 638–643

Keywords	Signal Processing ; Software ; Artificial Intelligence ; Computer Vision and Pattern Recognition ; covid19
Language	English
Publisher	Elsevier BV
Publishing country	us
Document type	Article ; Online
ISSN	0167-8655
DOI	10.1016/j.patrec.2020.09.010
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Human-level COVID-19 diagnosis from low-dose CT scans using a two-stage time-distributed capsule network.

Afshar, Parnian / Rafiee, Moezedin Javad / Naderkhani, Farnoosh / Heidarian, Shahin / Enshaei, Nastaran / Oikonomou, Anastasia / Babaki Fard, Faranak / Anconina, Reut / Farahani, Keyvan / Plataniotis, Konstantinos N / Mohammadi, Arash

Scientific reports

2022 Volume 12, Issue 1, Page(s) 4827

Abstract: Reverse transcription-polymerase chain reaction is currently the gold standard in COVID-19 diagnosis. It can, however, take days to provide the diagnosis, and false negative rate is relatively high. Imaging, in particular chest computed tomography (CT), ... ...

Abstract	Reverse transcription-polymerase chain reaction is currently the gold standard in COVID-19 diagnosis. It can, however, take days to provide the diagnosis, and false negative rate is relatively high. Imaging, in particular chest computed tomography (CT), can assist with diagnosis and assessment of this disease. Nevertheless, it is shown that standard dose CT scan gives significant radiation burden to patients, especially those in need of multiple scans. In this study, we consider low-dose and ultra-low-dose (LDCT and ULDCT) scan protocols that reduce the radiation exposure close to that of a single X-ray, while maintaining an acceptable resolution for diagnosis purposes. Since thoracic radiology expertise may not be widely available during the pandemic, we develop an Artificial Intelligence (AI)-based framework using a collected dataset of LDCT/ULDCT scans, to study the hypothesis that the AI model can provide human-level performance. The AI model uses a two stage capsule network architecture and can rapidly classify COVID-19, community acquired pneumonia (CAP), and normal cases, using LDCT/ULDCT scans. Based on a cross validation, the AI model achieves COVID-19 sensitivity of [Formula: see text], CAP sensitivity of [Formula: see text], normal cases sensitivity (specificity) of [Formula: see text], and accuracy of [Formula: see text]. By incorporating clinical data (demographic and symptoms), the performance further improves to COVID-19 sensitivity of [Formula: see text], CAP sensitivity of [Formula: see text], normal cases sensitivity (specificity) of [Formula: see text] , and accuracy of [Formula: see text]. The proposed AI model achieves human-level diagnosis based on the LDCT/ULDCT scans with reduced radiation exposure. We believe that the proposed AI model has the potential to assist the radiologists to accurately and promptly diagnose COVID-19 infection and help control the transmission chain during the pandemic.
MeSH term(s)	Artificial Intelligence ; COVID-19/diagnostic imaging ; COVID-19 Testing ; Humans ; Radionuclide Imaging ; Tomography, X-Ray Computed
Language	English
Publishing date	2022-03-22
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-022-08796-8
Database	MEDical Literature Analysis and Retrieval System OnLINE

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