pt-sk/ml_research_abs huggingface dataset:
SEL-COVIDNET: An intelligent application for the diagnosis of COVID-19 from chest X-rays and CT-scans.
Authors: Ahmad AlSmadi, AhedAbugabah, Ahmad MohammadAl-Smadi, SultanAlmotairi
Journal: Informatics in medicine unlocked

COVID-19 detection from medical imaging is a difficult challenge that has piqued the interest of experts worldwide. Chest X-rays and computed tomography (CT) scanning are the essential imaging modalities for diagnosing COVID-19. All researchers focus their efforts on developing viable methods and rapid treatment procedures for this pandemic. Fast and accurate automated detection approaches have been devised to alleviate the need for medical professionals. Deep Learning (DL) technologies have successfully recognized COVID-19 situations. This paper proposes a developed set of nine deep learning models for diagnosing COVID-19 based on transfer learning and implementation in a novel architecture (SEL-COVIDNET). We include a global average pooling layer, flattening, and two dense layers that are fully connected. The model's effectiveness is evaluated using balanced and unbalanced COVID-19 radiography datasets. After that, our model's performance is analyzed using six evaluation measures: accuracy, sensitivity, specificity, precision, F1-score, and Matthew's correlation coefficient (MCC). Experiments demonstrated that the proposed SEL-COVIDNET with tuned DenseNet121, InceptionResNetV2, and MobileNetV3Large models outperformed the results of comparative SOTA for multi-class classification (COVID-19 vs. No-finding vs. Pneumonia) in terms of accuracy (98.52%), specificity (98.5%), sensitivity (98.5%), precision (98.7%), F1-score (98.7%), and MCC (97.5%). For the COVID-19 vs. No-finding classification, our method had an accuracy of 99.77%, a specificity of 99.85%, a sensitivity of 99.85%, a precision of 99.55%, an F1-score of 99.7%, and an MCC of 99.4%. The proposed model offers an accurate approach for detecting COVID-19 patients, which aids in the containment of the COVID-19 pandemic.
HADCNet: Automatic segmentation of COVID-19 infection based on a hybrid attention dense connected network with dilated convolution.
Authors: YingChen, TaohuiZhou, YiChen, LongfengFeng, ChengZheng, LanLiu, LipingHu, BujianPan
Journal: Computers in biology and medicine

the automatic segmentation of lung infections in CT slices provides a rapid and effective strategy for diagnosing, treating, and assessing COVID-19 cases. However, the segmentation of the infected areas presents several difficulties, including high intraclass variability and interclass similarity among infected areas, as well as blurred edges and low contrast. Therefore, we propose HADCNet, a deep learning framework that segments lung infections based on a dual hybrid attention strategy. HADCNet uses an encoder hybrid attention module to integrate feature information at different scales across the peer hierarchy to refine the feature map. Furthermore, a decoder hybrid attention module uses an improved skip connection to embed the semantic information of higher-level features into lower-level features by integrating multi-scale contextual structures and assigning the spatial information of lower-level features to higher-level features, thereby capturing the contextual dependencies of lesion features across levels and refining the semantic structure, which reduces the semantic gap between feature maps at different levels and improves the model segmentation performance. We conducted fivefold cross-validations of our model on four publicly available datasets, with final mean Dice scores of 0.792, 0.796, 0.785, and 0.723. These results show that the proposed model outperforms popular state-of-the-art semantic segmentation methods and indicate its potential use in the diagnosis and treatment of COVID-19.
Novel Coronavirus and Common Pneumonia Detection from CT Scans Using Deep Learning-Based Extracted Features.
Authors: GhazanfarLatif, HamdyMorsy, AsmaaHassan, JaafarAlghazo
Journal: Viruses

COVID-19 which was announced as a pandemic on 11 March 2020, is still infecting millions to date as the vaccines that have been developed do not prevent the disease but rather reduce the severity of the symptoms. Until a vaccine is developed that can prevent COVID-19 infection, the testing of individuals will be a continuous process. Medical personnel monitor and treat all health conditions; hence, the time-consuming process to monitor and test all individuals for COVID-19 becomes an impossible task, especially as COVID-19 shares similar symptoms with the common cold and pneumonia. Some off-the-counter tests have been developed and sold, but they are unreliable and add an additional burden because false-positive cases have to visit hospitals and perform specialized diagnostic tests to confirm the diagnosis. Therefore, the need for systems that can automatically detect and diagnose COVID-19 automatically without human intervention is still an urgent priority and will remain so because the same technology can be used for future pandemics and other health conditions. In this paper, we propose a modified machine learning (ML) process that integrates deep learning (DL) algorithms for feature extraction and well-known classifiers that can accurately detect and diagnose COVID-19 from chest CT scans. Publicly available datasets were made available by the China Consortium for Chest CT Image Investigation (CC-CCII). The highest average accuracy obtained was 99.9% using the modified ML process when 2000 features were extracted using GoogleNet and ResNet18 and using the support vector machine (SVM) classifier. The results obtained using the modified ML process were higher when compared to similar methods reported in the extant literature using the same datasets or different datasets of similar size; thus, this study is considered of added value to the current body of knowledge. Further research in this field is required to develop methods that can be applied in hospitals and can better equip mankind to be prepared for any future pandemics.
Vascular Implications of COVID-19: Role of Radiological Imaging, Artificial Intelligence, and Tissue Characterization: A Special Report.
Authors: Narendra NKhanna, MaheshMaindarkar, AnudeepPuvvula, SudipPaul, MrinaliniBhagawati, PuneetAhluwalia, ZoltanRuzsa, AdityaSharma, SmikshaMunjral, RaghuKolluri, Padukone RKrishnan, Inder MSingh, John RLaird, MostafaFatemi, AzraAlizad, Surinder KDhanjil, LucaSaba, AntonellaBalestrieri, GavinoFaa, Kosmas IParaskevas, Durga PrasannaMisra, VikasAgarwal, AmanSharma, JagjitTeji, MustafaAl-Maini, AndrewNicolaides, VijayRathore, SubbaramNaidu, KieraLiblik, Amer MJohri, MonikaTurk, David WSobel, GyanPareek, MartinMiner, KlaudijaViskovic, GeorgeTsoulfas, Athanasios DProtogerou, SophieMavrogeni, George DKitas, Mostafa MFouda, Manudeep KKalra, Jasjit SSuri
Journal: Journal of cardiovascular development and disease

The SARS-CoV-2 virus has caused a pandemic, infecting nearly 80 million people worldwide, with mortality exceeding six million. The average survival span is just 14 days from the time the symptoms become aggressive. The present study delineates the deep-driven vascular damage in the pulmonary, renal, coronary, and carotid vessels due to SARS-CoV-2. This special report addresses an important gap in the literature in understanding (i) the pathophysiology of vascular damage and the role of medical imaging in the visualization of the damage caused by SARS-CoV-2, and (ii) further understanding the severity of COVID-19 using artificial intelligence (AI)-based tissue characterization (TC). PRISMA was used to select 296 studies for AI-based TC. Radiological imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound were selected for imaging of the vasculature infected by COVID-19. Four kinds of hypotheses are presented for showing the vascular damage in radiological images due to COVID-19. Three kinds of AI models, namely, machine learning, deep learning, and transfer learning, are used for TC. Further, the study presents recommendations for improving AI-based architectures for vascular studies. We conclude that the process of vascular damage due to COVID-19 has similarities across vessel types, even though it results in multi-organ dysfunction. Although the mortality rate is ~2% of those infected, the long-term effect of COVID-19 needs monitoring to avoid deaths. AI seems to be penetrating the health care industry at warp speed, and we expect to see an emerging role in patient care, reduce the mortality and morbidity rate.
Artificial intelligence model on chest imaging to diagnose COVID-19 and other pneumonias: A systematic review and meta-analysis.
Authors: Lu-LuJia, Jian-XinZhao, Ni-NiPan, Liu-YanShi, Lian-PingZhao, Jin-HuiTian, GangHuang
Journal: European journal of radiology open

When diagnosing Coronavirus disease 2019(COVID-19), radiologists cannot make an accurate judgments because the image characteristics of COVID-19 and other pneumonia are similar. As machine learning advances, artificial intelligence(AI) models show promise in diagnosing COVID-19 and other pneumonias. We performed a systematic review and meta-analysis to assess the diagnostic accuracy and methodological quality of the models.
We searched PubMed, Cochrane Library, Web of Science, and Embase, preprints from medRxiv and bioRxiv to locate studies published before December 2021, with no language restrictions. And a quality assessment (QUADAS-2), Radiomics Quality Score (RQS) tools and CLAIM checklist were used to assess the quality of each study. We used random-effects models to calculate pooled sensitivity and specificity, I
We screened 32 studies from the 2001 retrieved articles for inclusion in the meta-analysis. We included 6737 participants in the test or validation group. The meta-analysis revealed that AI models based on chest imaging distinguishes COVID-19 from other pneumonias: pooled area under the curve (AUC) 0.96 (95 % CI, 0.94-0.98), sensitivity 0.92 (95 % CI, 0.88-0.94), pooled specificity 0.91 (95 % CI, 0.87-0.93). The average RQS score of 13 studies using radiomics was 7.8, accounting for 22 % of the total score. The 19 studies using deep learning methods had an average CLAIM score of 20, slightly less than half (48.24 %) the ideal score of 42.00.
The AI model for chest imaging could well diagnose COVID-19 and other pneumonias. However, it has not been implemented as a clinical decision-making tool. Future researchers should pay more attention to the quality of research methodology and further improve the generalizability of the developed predictive models.
A dual-stage deep convolutional neural network for automatic diagnosis of COVID-19 and pneumonia from chest CT images.
Authors: FarhanSadik, Ankan GhoshDastider, Mohseu RashidSubah, TanvirMahmud, Shaikh AnowarulFattah
Journal: Computers in biology and medicine

In the Coronavirus disease-2019 (COVID-19) pandemic, for fast and accurate diagnosis of a large number of patients, besides traditional methods, automated diagnostic tools are now extremely required. In this paper, a deep convolutional neural network (CNN) based scheme is proposed for automated accurate diagnosis of COVID-19 from lung computed tomography (CT) scan images. First, for the automated segmentation of lung regions in a chest CT scan, a modified CNN architecture, namely SKICU-Net is proposed by incorporating additional skip interconnections in the U-Net model that overcome the loss of information in dimension scaling. Next, an agglomerative hierarchical clustering is deployed to eliminate the CT slices without significant information. Finally, for effective feature extraction and diagnosis of COVID-19 and pneumonia from the segmented lung slices, a modified DenseNet architecture, namely P-DenseCOVNet is designed where parallel convolutional paths are introduced on top of the conventional DenseNet model for getting better performance through overcoming the loss of positional arguments. Outstanding performances have been achieved with an F
Multicenter Study on COVID-19 Lung Computed Tomography Segmentation with varying Glass Ground Opacities using Unseen Deep Learning Artificial Intelligence Paradigms: COVLIAS 1.0 Validation.
Authors: Jasjit SSuri, SushantAgarwal, LucaSaba, Gian LucaChabert, AlessandroCarriero, AlessioPaschè, PietroDanna, ArminMehmedović, GavinoFaa, TanayJujaray, Inder MSingh, Narendra NKhanna, John RLaird, Petros PSfikakis, VikasAgarwal, Jagjit STeji, RajanikantR Yadav, FerencNagy, Zsigmond TamásKincses, ZoltanRuzsa, KlaudijaViskovic, Mannudeep KKalra
Journal: Journal of medical systems

Variations in COVID-19 lesions such as glass ground opacities (GGO), consolidations, and crazy paving can compromise the ability of solo-deep learning (SDL) or hybrid-deep learning (HDL) artificial intelligence (AI) models in predicting automated COVID-19 lung segmentation in Computed Tomography (CT) from unseen data leading to poor clinical manifestations. As the first study of its kind, "COVLIAS 1.0-Unseen" proves two hypotheses, (i) contrast adjustment is vital for AI, and (ii) HDL is superior to SDL. In a multicenter study, 10,000 CT slices were collected from 72 Italian (ITA) patients with low-GGO, and 80 Croatian (CRO) patients with high-GGO. Hounsfield Units (HU) were automatically adjusted to train the AI models and predict from test data, leading to four combinations-two Unseen sets: (i) train-CRO:test-ITA, (ii) train-ITA:test-CRO, and two Seen sets: (iii) train-CRO:test-CRO, (iv) train-ITA:test-ITA. COVILAS used three SDL models: PSPNet, SegNet, UNet and six HDL models: VGG-PSPNet, VGG-SegNet, VGG-UNet, ResNet-PSPNet, ResNet-SegNet, and ResNet-UNet. Two trained, blinded senior radiologists conducted ground truth annotations. Five types of performance metrics were used to validate COVLIAS 1.0-Unseen which was further benchmarked against MedSeg, an open-source web-based system. After HU adjustment for DS and JI, HDL (Unseen AI) > SDL (Unseen AI) by 4% and 5%, respectively. For CC, HDL (Unseen AI) > SDL (Unseen AI) by 6%. The COVLIAS-MedSeg difference was < 5%, meeting regulatory guidelines.Unseen AI was successfully demonstrated using automated HU adjustment. HDL was found to be superior to SDL.
Psoas muscle metastatic disease mimicking a psoas abscess on imaging.
Authors: ChristopherGunn, MazyarFani
Journal: BMJ case reports

Here, we report a case of malignant psoas syndrome presented to us during the second peak of the COVID-19 pandemic. Our patient had a medical history of hypertension, recently diagnosed with left iliac deep vein thrombosis and previous breast and endometrial cancers. She presented with exquisite pain and a fixed flexion deformity of the left hip. A rim-enhancing lesion was seen within the left psoas muscle and was initially deemed to be a psoas abscess. This failed to respond to medical management and attempts at drainage. Subsequent further imaging revealed the mass was of a malignant nature; histology revealing a probable carcinomatous origin. Following diagnosis, palliative input was obtained and, unfortunately, our patient passed away in a hospice shortly after discharge. We discuss the aetiology, radiological findings and potential treatments of this condition and learning points to prompt clinicians to consider this diagnosis in those with a personal history of cancer.
Rapid tissue prototyping with micro-organospheres.
Authors: ZhaohuiWang, MatteoBoretto, RosemaryMillen, NaveenNatesh, Elena SReckzeh, CarolynHsu, MarcosNegrete, HaipeiYao, WilliamQuayle, Brook EHeaton, Alfred THarding, ShreeBose, ElseDriehuis, JoepBeumer, Grecia ORivera, Ravian Lvan Ineveld, DonaldGex, JessicaDeVilla, DaisongWang, JensPuschhof, Maarten HGeurts, AthenaYeung, CaitHamele, AmberSmith, EricBankaitis, KunXiang, ShengliDing, DanielNelson, DanielDelubac, AnneRios, RalphAbi-Hachem, DavidJang, Bradley JGoldstein, CarolynGlass, Nicholas SHeaton, DavidHsu, HansClevers, XilingShen
Journal: Stem cell reports

In vitro tissue models hold great promise for modeling diseases and drug responses. Here, we used emulsion microfluidics to form micro-organospheres (MOSs), which are droplet-encapsulated miniature three-dimensional (3D) tissue models that can be established rapidly from patient tissues or cells. MOSs retain key biological features and responses to chemo-, targeted, and radiation therapies compared with organoids. The small size and large surface-to-volume ratio of MOSs enable various applications including quantitative assessment of nutrient dependence, pathogen-host interaction for anti-viral drug screening, and a rapid potency assay for chimeric antigen receptor (CAR)-T therapy. An automated MOS imaging pipeline combined with machine learning overcomes plating variation, distinguishes tumorspheres from stroma, differentiates cytostatic versus cytotoxic drug effects, and captures resistant clones and heterogeneity in drug response. This pipeline is capable of robust assessments of drug response at individual-tumorsphere resolution and provides a rapid and high-throughput therapeutic profiling platform for precision medicine.
DAFLNet: Dual Asymmetric Feature Learning Network for COVID-19 Disease Diagnosis in X-Rays.
Authors: JingyaoLiu, JiashiZhao, LiyuanZhang, YuMiao, WeiHe, WeiliShi, YanfangLi, BaiJi, KeZhang, ZhengangJiang
Journal: Computational and mathematical methods in medicine

COVID-19 has become the largest public health event worldwide since its outbreak, and early detection is a prerequisite for effective treatment. Chest X-ray images have become an important basis for screening and monitoring the disease, and deep learning has shown great potential for this task. Many studies have proposed deep learning methods for automated diagnosis of COVID-19. Although these methods have achieved excellent performance in terms of detection, most have been evaluated using limited datasets and typically use a single deep learning network to extract features. To this end, the dual asymmetric feature learning network (DAFLNet) is proposed, which is divided into two modules, DAFFM and WDFM. DAFFM mainly comprises the backbone networks EfficientNetV2 and DenseNet for feature fusion. WDFM is mainly for weighted decision-level fusion and features a new pretrained network selection algorithm (PNSA) for determination of the optimal weights. Experiments on a large dataset were conducted using two schemes, DAFLNet-1 and DAFLNet-2, and both schemes outperformed eight state-of-the-art classification techniques in terms of classification performance. DAFLNet-1 achieved an average accuracy of up to 98.56% for the triple classification of COVID-19, pneumonia, and healthy images.
MFL-Net: An Efficient Lightweight Multi-Scale Feature Learning CNN for COVID-19 Diagnosis From CT Images.
Authors: Amogh ManojJoshi, Deepak RanjanNayak
Journal: IEEE journal of biomedical and health informatics

Timely and accurate diagnosis of coronavirus disease 2019 (COVID-19) is crucial in curbing its spread. Slow testing results of reverse transcription-polymerase chain reaction (RT-PCR) and a shortage of test kits have led to consider chest computed tomography (CT) as an alternative screening and diagnostic tool. Many deep learning methods, especially convolutional neural networks (CNNs), have been developed to detect COVID-19 cases from chest CT scans. Most of these models demand a vast number of parameters which often suffer from overfitting in the presence of limited training data. Moreover, the linearly stacked single-branched architecture based models hamper the extraction of multi-scale features, reducing the detection performance. In this paper, to handle these issues, we propose an extremely lightweight CNN with multi-scale feature learning blocks called as MFL-Net. The MFL-Net comprises a sequence of MFL blocks that combines multiple convolutional layers with 3 ×3 filters and residual connections effectively, thereby extracting multi-scale features at different levels and preserving them throughout the block. The model has only 0.78M parameters and requires low computational cost and memory space compared to many ImageNet pretrained CNN architectures. Comprehensive experiments are carried out using two publicly available COVID-19 CT imaging datasets. The results demonstrate that the proposed model achieves higher performance than pretrained CNN models and state-of-the-art methods on both datasets with limited training data despite having an extremely lightweight architecture. The proposed method proves to be an effective aid for the healthcare system in the accurate and timely diagnosis of COVID-19.
Semi-supervised COVID-19 CT image segmentation using deep generative models.
Authors: JudahZammit, Daryl L XFung, QianLiu, Carson Kai-SangLeung, PingzhaoHu
Journal: BMC bioinformatics

A recurring problem in image segmentation is a lack of labelled data. This problem is especially acute in the segmentation of lung computed tomography (CT) of patients with Coronavirus Disease 2019 (COVID-19). The reason for this is simple: the disease has not been prevalent long enough to generate a great number of labels. Semi-supervised learning promises a way to learn from data that is unlabelled and has seen tremendous advancements in recent years. However, due to the complexity of its label space, those advancements cannot be applied to image segmentation. That being said, it is this same complexity that makes it extremely expensive to obtain pixel-level labels, making semi-supervised learning all the more appealing. This study seeks to bridge this gap by proposing a novel model that utilizes the image segmentation abilities of deep convolution networks and the semi-supervised learning abilities of generative models for chest CT images of patients with the COVID-19.
We propose a novel generative model called the shared variational autoencoder (SVAE). The SVAE utilizes a five-layer deep hierarchy of latent variables and deep convolutional mappings between them, resulting in a generative model that is well suited for lung CT images. Then, we add a novel component to the final layer of the SVAE which forces the model to reconstruct the input image using a segmentation that must match the ground truth segmentation whenever it is present. We name this final model StitchNet.
We compare StitchNet to other image segmentation models on a high-quality dataset of CT images from COVID-19 patients. We show that our model has comparable performance to the other segmentation models. We also explore the potential limitations and advantages in our proposed algorithm and propose some potential future research directions for this challenging issue.
CovMnet-Deep Learning Model for classifying Coronavirus (COVID-19).
Authors: MalathyJawahar, Jani AnbarasiL, VinayakumarRavi, JPrassanna, S GracelineJasmine, RManikandan, RamesSekaran, SuthendranKannan
Journal: Health and technology

Diagnosing COVID-19, current pandemic disease using Chest X-ray images is widely used to evaluate the lung disorders. As the spread of the disease is enormous many medical camps are being conducted to screen the patients and Chest X-ray is a simple imaging modality to detect presence of lung disorders. Manual lung disorder detection using Chest X-ray by radiologist is a tedious process and may lead to inter and intra-rate errors. Various deep convolution neural network techniques were tested for detecting COVID-19 abnormalities in lungs using Chest X-ray images. This paper proposes deep learning model to classify COVID-19 and normal chest X-ray images. Experiments are carried out for deep feature extraction, fine-tuning of convolutional neural networks (CNN) hyper parameters, and end-to-end training of four variants of the CNN model. The proposed CovMnet provide better classification accuracy of 97.4% for COVID-19 /normal than those reported in the previous studies. The proposed CovMnet model has potential to aid radiologist to monitor COVID-19 disease and proves to be an efficient non-invasive COVID-19 diagnostic tool for lung disorders.