pt-sk/ml_research_abs huggingface dataset:
COVID-19 pathways for brain and heart injury in comorbidity patients: A role of medical imaging and artificial intelligence-based COVID severity classification: A review.
Authors: Jasjit SSuri, AnudeepPuvvula, MainakBiswas, MishaMajhail, LucaSaba, GavinoFaa, Inder MSingh, RonaldOberleitner, MonikaTurk, Paramjit SChadha, Amer MJohri, J MiguelSanches, Narendra NKhanna, KlaudijaViskovic, SophieMavrogeni, John RLaird, GyanPareek, MartinMiner, David WSobel, AntonellaBalestrieri, Petros PSfikakis, GeorgeTsoulfas, AthanasiosProtogerou, Durga PrasannaMisra, VikasAgarwal, George DKitas, PuneetAhluwalia, RaghuKolluri, JagjitTeji, Mustafa AlMaini, AnnAgbakoba, Surinder KDhanjil, MeyypanSockalingam, AjitSaxena, AndrewNicolaides, AdityaSharma, VijayRathore, Janet N AAjuluchukwu, MostafaFatemi, AzraAlizad, VijayViswanathan, Pudukode RKrishnan, SubbaramNaidu
Journal: Computers in biology and medicine

Artificial intelligence (AI) has penetrated the field of medicine, particularly the field of radiology. Since its emergence, the highly virulent coronavirus disease 2019 (COVID-19) has infected over 10 million people, leading to over 500,000 deaths as of July 1st, 2020. Since the outbreak began, almost 28,000 articles about COVID-19 have been published (https://pubmed.ncbi.nlm.nih.gov); however, few have explored the role of imaging and artificial intelligence in COVID-19 patients-specifically, those with comorbidities. This paper begins by presenting the four pathways that can lead to heart and brain injuries following a COVID-19 infection. Our survey also offers insights into the role that imaging can play in the treatment of comorbid patients, based on probabilities derived from COVID-19 symptom statistics. Such symptoms include myocardial injury, hypoxia, plaque rupture, arrhythmias, venous thromboembolism, coronary thrombosis, encephalitis, ischemia, inflammation, and lung injury. At its core, this study considers the role of image-based AI, which can be used to characterize the tissues of a COVID-19 patient and classify the severity of their infection. Image-based AI is more important than ever as the pandemic surges and countries worldwide grapple with limited medical resources for detection and diagnosis.
Contrastive Cross-Site Learning With Redesigned Net for COVID-19 CT Classification.
Authors: ZhaoWang, QuandeLiu, QiDou
Journal: IEEE journal of biomedical and health informatics

The pandemic of coronavirus disease 2019 (COVID-19) has lead to a global public health crisis spreading hundreds of countries. With the continuous growth of new infections, developing automated tools for COVID-19 identification with CT image is highly desired to assist the clinical diagnosis and reduce the tedious workload of image interpretation. To enlarge the datasets for developing machine learning methods, it is essentially helpful to aggregate the cases from different medical systems for learning robust and generalizable models. This paper proposes a novel joint learning framework to perform accurate COVID-19 identification by effectively learning with heterogeneous datasets with distribution discrepancy. We build a powerful backbone by redesigning the recently proposed COVID-Net in aspects of network architecture and learning strategy to improve the prediction accuracy and learning efficiency. On top of our improved backbone, we further explicitly tackle the cross-site domain shift by conducting separate feature normalization in latent space. Moreover, we propose to use a contrastive training objective to enhance the domain invariance of semantic embeddings for boosting the classification performance on each dataset. We develop and evaluate our method with two public large-scale COVID-19 diagnosis datasets made up of CT images. Extensive experiments show that our approach consistently improves the performanceson both datasets, outperforming the original COVID-Net trained on each dataset by 12.16% and 14.23% in AUC respectively, also exceeding existing state-of-the-art multi-site learning methods.
Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network.
Authors: AsmaaAbbas, Mohammed MAbdelsamea, Mohamed MedhatGaber
Journal: Applied intelligence (Dordrecht, Netherlands)

Chest X-ray is the first imaging technique that plays an important role in the diagnosis of COVID-19 disease. Due to the high availability of large-scale annotated image datasets, great success has been achieved using convolutional neural networks (
Ultra-low-dose chest CT imaging of COVID-19 patients using a deep residual neural network.
Authors: IsaacShiri, AzadehAkhavanallaf, AmirhosseinSanaat, YazdanSalimi, DariushAskari, ZahraMansouri, Sajad PShayesteh, MohammadHasanian, KiaraRezaei-Kalantari, AliSalahshour, SalehSandoughdaran, HamidAbdollahi, HosseinArabi, HabibZaidi
Journal: European radiology

The current study aimed to design an ultra-low-dose CT examination protocol using a deep learning approach suitable for clinical diagnosis of COVID-19 patients.
In this study, 800, 170, and 171 pairs of ultra-low-dose and full-dose CT images were used as input/output as training, test, and external validation set, respectively, to implement the full-dose prediction technique. A residual convolutional neural network was applied to generate full-dose from ultra-low-dose CT images. The quality of predicted CT images was assessed using root mean square error (RMSE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR). Scores ranging from 1 to 5 were assigned reflecting subjective assessment of image quality and related COVID-19 features, including ground glass opacities (GGO), crazy paving (CP), consolidation (CS), nodular infiltrates (NI), bronchovascular thickening (BVT), and pleural effusion (PE).
The radiation dose in terms of CT dose index (CTDI
The results demonstrated that the deep learning algorithm is capable of predicting standard full-dose CT images with acceptable quality for the clinical diagnosis of COVID-19 positive patients with substantial radiation dose reduction.
• Ultra-low-dose CT imaging of COVID-19 patients would result in the loss of critical information about lesion types, which could potentially affect clinical diagnosis. • Deep learning-based prediction of full-dose from ultra-low-dose CT images for the diagnosis of COVID-19 could reduce the radiation dose by up to 89%. • Deep learning algorithms failed to recover the correct lesion structure/density for a number of patients considered outliers, and as such, further research and development is warranted to address these limitations.
Visual and software-based quantitative chest CT assessment of COVID-19: correlation with clinical findings.
Authors: GamzeDurhan, SelinArdalı Düzgün, FigenBaşaran Demirkazık, İlimIrmak, İlkayİdilman, MeltemGülsün Akpınar, ErhanAkpınar, SerpilÖcal, GülçinTelli, ArzuTopeli, Orhan MacitArıyürek
Journal: Diagnostic and interventional radiology (Ankara, Turkey)

The aim of this study was to evaluate visual and software-based quantitative assessment of parenchymal changes and normal lung parenchyma in patients with coronavirus disease 2019 (COVID-19) pneumonia. The secondary aim of the study was to compare the radiologic findings with clinical and laboratory data.
Patients with COVID-19 who underwent chest computed tomography (CT) between March 11, 2020 and April 15, 2020 were retrospectively evaluated. Clinical and laboratory findings of patients with abnormal findings on chest CT and PCR-evidence of COVID-19 infection were recorded. Visual quantitative assessment score (VQAS) was performed according to the extent of lung opacities. Software-based quantitative assessment of the normal lung parenchyma percentage (SQNLP) was automatically quantified by a deep learning software. The presence of consolidation and crazy paving pattern (CPP) was also recorded. Statistical analyses were performed to evaluate the correlation between quantitative radiologic assessments, and clinical and laboratory findings, as well as to determine the predictive utility of radiologic findings for estimating severe pneumonia and admission to intensive care unit (ICU).
A total of 90 patients were enrolled. Both VQAS and SQNLP were significantly correlated with multiple clinical parameters. While VQAS >8.5 (sensitivity, 84.2%; specificity, 80.3%) and SQNLP <82.45% (sensitivity, 83.1%; specificity, 84.2%) were related to severe pneumonia, VQAS >9.5 (sensitivity, 93.3%; specificity, 86.5%) and SQNLP <81.1% (sensitivity, 86.5%; specificity, 86.7%) were predictive of ICU admission. Both consolidation and CPP were more commonly seen in patients with severe pneumonia than patients with nonsevere pneumonia (P = 0.197 for consolidation; P < 0.001 for CPP). Moreover, the presence of CPP showed high specificity (97.2%) for severe pneumonia.
Both SQNLP and VQAS were significantly related to the clinical findings, highlighting their clinical utility in predicting severe pneumonia, ICU admission, length of hospital stay, and management of the disease. On the other hand, presence of CPP has high specificity for severe COVID-19 pneumonia.
Toward automated severe pharyngitis detection with smartphone camera using deep learning networks.
Authors: Tae KeunYoo, Joon YulChoi, YounilJang, EinOh, Ik HeeRyu
Journal: Computers in biology and medicine

Severe pharyngitis is frequently associated with inflammations caused by streptococcal pharyngitis, which can cause immune-mediated and post-infectious complications. The recent global pandemic of coronavirus disease (COVID-19) encourages the use of telemedicine for patients with respiratory symptoms. This study, therefore, purposes automated detection of severe pharyngitis using a deep learning framework with self-taken throat images.
A dataset composed of two classes of 131 throat images with pharyngitis and 208 normal throat images was collected. Before the training classifier, we constructed a cycle consistency generative adversarial network (CycleGAN) to augment the training dataset. The ResNet50, Inception-v3, and MobileNet-v2 architectures were trained with transfer learning and validated using a randomly selected test dataset. The performance of the models was evaluated based on the accuracy and area under the receiver operating characteristic curve (ROC-AUC).
The CycleGAN-based synthetic images reflected the pragmatic characteristic features of pharyngitis. Using the synthetic throat images, the deep learning model demonstrated a significant improvement in the accuracy of the pharyngitis diagnosis. ResNet50 with GAN-based augmentation showed the best ROC-AUC of 0.988 for pharyngitis detection in the test dataset. In the 4-fold cross-validation using the ResNet50, the highest detection accuracy and ROC-AUC achieved were 95.3% and 0.992, respectively.
The deep learning model for smartphone-based pharyngitis screening allows fast identification of severe pharyngitis with a potential of the timely diagnosis of pharyngitis. In the recent pandemic of COVID-19, this framework will help patients with upper respiratory symptoms to improve convenience in diagnosis and reduce transmission.
Development and Validation of a Deep Learning-Based Model Using Computed Tomography Imaging for Predicting Disease Severity of Coronavirus Disease 2019.
Authors: Lu-ShanXiao, PuLi, FenglongSun, YanpeiZhang, ChenghaiXu, HongboZhu, Feng-QinCai, Yu-LinHe, Wen-FengZhang, Si-CongMa, ChenyiHu, MengchunGong, LiLiu, WenzhaoShi, HongZhu
Journal: Frontiers in bioengineering and biotechnology

Coronavirus disease 2019 (COVID-19) is sweeping the globe and has resulted in infections in millions of people. Patients with COVID-19 face a high fatality risk once symptoms worsen; therefore, early identification of severely ill patients can enable early intervention, prevent disease progression, and help reduce mortality. This study aims to develop an artificial intelligence-assisted tool using computed tomography (CT) imaging to predict disease severity and further estimate the risk of developing severe disease in patients suffering from COVID-19.
Initial CT images of 408 confirmed COVID-19 patients were retrospectively collected between January 1, 2020 and March 18, 2020 from hospitals in Honghu and Nanchang. The data of 303 patients in the People's Hospital of Honghu were assigned as the training data, and those of 105 patients in The First Affiliated Hospital of Nanchang University were assigned as the test dataset. A deep learning based-model using multiple instance learning and residual convolutional neural network (ResNet34) was developed and validated. The discrimination ability and prediction accuracy of the model were evaluated using the receiver operating characteristic curve and confusion matrix, respectively.
The deep learning-based model had an area under the curve (AUC) of 0.987 (95% confidence interval [CI]: 0.968-1.00) and an accuracy of 97.4% in the training set, whereas it had an AUC of 0.892 (0.828-0.955) and an accuracy of 81.9% in the test set. In the subgroup analysis of patients who had non-severe COVID-19 on admission, the model achieved AUCs of 0.955 (0.884-1.00) and 0.923 (0.864-0.983) and accuracies of 97.0 and 81.6% in the Honghu and Nanchang subgroups, respectively.
Our deep learning-based model can accurately predict disease severity as well as disease progression in COVID-19 patients using CT imaging, offering promise for guiding clinical treatment.
Digital pathology and computational image analysis in nephropathology.
Authors: LauraBarisoni, Kyle JLafata, Stephen MHewitt, AnantMadabhushi, Ulysses G JBalis
Journal: Nature reviews. Nephrology

The emergence of digital pathology - an image-based environment for the acquisition, management and interpretation of pathology information supported by computational techniques for data extraction and analysis - is changing the pathology ecosystem. In particular, by virtue of our new-found ability to generate and curate digital libraries, the field of machine vision can now be effectively applied to histopathological subject matter by individuals who do not have deep expertise in machine vision techniques. Although these novel approaches have already advanced the detection, classification, and prognostication of diseases in the fields of radiology and oncology, renal pathology is just entering the digital era, with the establishment of consortia and digital pathology repositories for the collection, analysis and integration of pathology data with other domains. The development of machine-learning approaches for the extraction of information from image data, allows for tissue interrogation in a way that was not previously possible. The application of these novel tools are placing pathology centre stage in the process of defining new, integrated, biologically and clinically homogeneous disease categories, to identify patients at risk of progression, and shifting current paradigms for the treatment and prevention of kidney diseases.
Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification With Chest CT.
Authors: LiangSun, ZhanhaoMo, FuhuaYan, LimingXia, FeiShan, ZhongxiangDing, BinSong, WanchunGao, WeiShao, FengShi, HuanYuan, HuitingJiang, DijiaWu, YingWei, YaozongGao, HeSui, DaoqiangZhang, DinggangShen
Journal: IEEE journal of biomedical and health informatics

Chest computed tomography (CT) becomes an effective tool to assist the diagnosis of coronavirus disease-19 (COVID-19). Due to the outbreak of COVID-19 worldwide, using the computed-aided diagnosis technique for COVID-19 classification based on CT images could largely alleviate the burden of clinicians. In this paper, we propose an Adaptive Feature Selection guided Deep Forest (AFS-DF) for COVID-19 classification based on chest CT images. Specifically, we first extract location-specific features from CT images. Then, in order to capture the high-level representation of these features with the relatively small-scale data, we leverage a deep forest model to learn high-level representation of the features. Moreover, we propose a feature selection method based on the trained deep forest model to reduce the redundancy of features, where the feature selection could be adaptively incorporated with the COVID-19 classification model. We evaluated our proposed AFS-DF on COVID-19 dataset with 1495 patients of COVID-19 and 1027 patients of community acquired pneumonia (CAP). The accuracy (ACC), sensitivity (SEN), specificity (SPE), AUC, precision and F1-score achieved by our method are 91.79%, 93.05%, 89.95%, 96.35%, 93.10% and 93.07%, respectively. Experimental results on the COVID-19 dataset suggest that the proposed AFS-DF achieves superior performance in COVID-19 vs. CAP classification, compared with 4 widely used machine learning methods.
A systematic review on recent trends in transmission, diagnosis, prevention and imaging features of COVID-19.
Authors: SManigandan, Ming-TsangWu, Vinoth KumarPonnusamy, Vinay BRaghavendra, ArivalaganPugazhendhi, KathirvelBrindhadevi
Journal: Process biochemistry (Barking, London, England)

As the new cases of COVID-19 are growing every daysince January 2020, the major way to control the spread wasthrough early diagnosis. Prevention and early diagnosis are the key strategies followed by most countries. This study presents the perspective of different modes of transmission of coronavirus,especially during clinical practices and among the pediatrics. Further, the diagnostic methods and the advancement of the computerized tomography have been discussed. Droplets, aerosol, and close contact are thesignificantfactors to transfer the infection to the suspect. This study predicts the possible transmission of the virus through medical practices such as ophthalmology, dental, and endoscopy procedures. With regard to pediatric transmission, as of now, only afew child fatalities had been reported. Childrenusually respond to the respiratory virus; however, COVID-19 response ison the contrary. The possibility of getting infected is minimal for the newborn. There has been no asymptomatic spread in children until now. Moreover, breastfeedingwould not transmit COVID-19, which is encouraging hygiene news for the pediatric. In addition, the current diagnostic methods for COVID-19 including Immunoglobulin M (IgM) and Immunoglobulin G (IgG)and chest computed topography(CT) scan, reverse transcription-polymerase chain reaction (RT-PCR) andimmunochromatographic fluorescence assay, are also discussed in detail. The introduction of artificial intelligence and deep learning algorithmhas the ability to diagnose COVID-19 in precise. However, the developments of a potential technology for the identification of the infection, such as a drone with thermal screening without human intervention, need to be encouraged.
[Research on coronavirus disease 2019 (COVID-19) detection method based on depthwise separable DenseNet in chest X-ray images].
Authors: YiboFeng, DaweiQiu, HuiCao, JunzhongZhang, ZaihaiXin, JingLiu
Journal: Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi

Coronavirus disease 2019 (COVID-19) has spread rapidly around the world. In order to diagnose COVID-19 more quickly, in this paper, a depthwise separable DenseNet was proposed. The paper constructed a deep learning model with 2 905 chest X-ray images as experimental dataset. In order to enhance the contrast, the contrast limited adaptive histogram equalization (CLAHE) algorithm was used to preprocess the X-ray image before network training, then the images were put into the training network and the parameters of the network were adjusted to the optimal. Meanwhile, Leaky ReLU was selected as the activation function. VGG16, ResNet18, ResNet34, DenseNet121 and SDenseNet models were used to compare with the model proposed in this paper. Compared with ResNet34, the proposed classification model of pneumonia had improved 2.0%, 2.3% and 1.5% in accuracy, sensitivity and specificity respectively. Compared with the SDenseNet network without depthwise separable convolution, number of parameters of the proposed model was reduced by 43.9%, but the classification effect did not decrease. It can be found that the proposed DWSDenseNet has a good classification effect on the COVID-19 chest X-ray images dataset. Under the condition of ensuring the accuracy as much as possible, the depthwise separable convolution can effectively reduce number of parameters of the model.
新型冠状病毒肺炎肆虐全球，为了更加快速地诊断新型冠状病毒肺炎（COVID-19），本文提出一种深度可分离稠密网络 DWSDenseNet，以 2 905 例 COVID-19 胸部 X 线平片影像作为实验数据集，在网络训练前使用限制对比度自适应直方图均衡化（CLAHE）算法对图像进行预处理，增强图像的对比度，将预处理之后的图像放入训练网络中，采用 Leaky ReLU 作为激活函数，调整参数以达到最优。本文引入 VGG16、ResNet18、ResNet34、DenseNet121 和 SDenseNet 模型进行比较，所提出的网络在三分类实验中相较于 ResNet34 在准确率、灵敏度和特异性上分别提高了 2.0%、2.3%、1.5%。相对于改进前的 SDenseNet 网络，本文模型的参数量减少了 43.9%，但分类效果并未下降。通过对比实验可以发现，本文所提出的深度可分离稠密网络对 COVID-19 胸部 X 线平片影像数据集具有良好的分类效果，在保证准确率的情况下，深度可分离卷积能够有效地降低模型参数量。.
A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia.
Authors: XiaoweiXu, XiangaoJiang, ChunlianMa, PengDu, XukunLi, ShuangzhiLv, LiangYu, QinNi, YanfeiChen, JunweiSu, GuanjingLang, YongtaoLi, HongZhao, JunLiu, KaijinXu, LingxiangRuan, JifangSheng, YunqingQiu, WeiWu, TingboLiang, LanjuanLi
Journal: Engineering (Beijing, China)

The real-time reverse transcription-polymerase chain reaction (RT-PCR) detection of viral RNA from sputum or nasopharyngeal swab had a relatively low positive rate in the early stage of coronavirus disease 2019 (COVID-19). Meanwhile, the manifestations of COVID-19 as seen through computed tomography (CT) imaging show individual characteristics that differ from those of other types of viral pneumonia such as influenza-A viral pneumonia (IAVP). This study aimed to establish an early screening model to distinguish COVID-19 from IAVP and healthy cases through pulmonary CT images using deep learning techniques. A total of 618 CT samples were collected: 219 samples from 110 patients with COVID-19 (mean age 50 years; 63 (57.3%) male patients); 224 samples from 224 patients with IAVP (mean age 61 years; 156 (69.6%) male patients); and 175 samples from 175 healthy cases (mean age 39 years; 97 (55.4%) male patients). All CT samples were contributed from three COVID-19-designated hospitals in Zhejiang Province, China. First, the candidate infection regions were segmented out from the pulmonary CT image set using a 3D deep learning model. These separated images were then categorized into the COVID-19, IAVP, and irrelevant to infection (ITI) groups, together with the corresponding confidence scores, using a location-attention classification model. Finally, the infection type and overall confidence score for each CT case were calculated using the Noisy-OR Bayesian function. The experimental result of the benchmark dataset showed that the overall accuracy rate was 86.7% in terms of all the CT cases taken together. The deep learning models established in this study were effective for the early screening of COVID-19 patients and were demonstrated to be a promising supplementary diagnostic method for frontline clinical doctors.