pt-sk/ml_research_abs huggingface dataset:
Predisposing Variations in Fear-Related Brain Networks Prospectively Predict Fearful Feelings during the 2019 Coronavirus (COVID-19) Pandemic.
Authors: PanFeng, ZhiyiChen, BenjaminBecker, XiqinLiu, FengZhou, QinghuaHe, JiangQiu, XuLei, HongChen, TingyongFeng
Journal: Cerebral cortex (New York, N.Y. : 1991)

The novel coronavirus (COVID-19) pandemic has led to a surge in mental distress and fear-related disorders, including posttraumatic stress disorder (PTSD). Fear-related disorders are characterized by dysregulations in fear and the associated neural pathways. In the present study, we examined whether individual variations in the fear neural connectome can predict fear-related symptoms during the COVID-19 pandemic. Using machine learning algorithms and back-propagation artificial neural network (BP-ANN) deep learning algorithms, we demonstrated that the intrinsic neural connectome before the COVID-19 pandemic could predict who would develop high fear-related symptoms at the peak of the COVID-19 pandemic in China (Accuracy rate = 75.00%, Sensitivity rate = 65.83%, Specificity rate = 84.17%). More importantly, prediction models could accurately predict the level of fear-related symptoms during the COVID-19 pandemic by using the prepandemic connectome state, in which the functional connectivity of lvmPFC (left ventromedial prefrontal cortex)-rdlPFC (right dorsolateral), rdACC (right dorsal anterior cingulate cortex)-left insula, lAMY (left amygdala)-lHip (left hippocampus) and lAMY-lsgACC (left subgenual cingulate cortex) was contributed to the robust prediction. The current study capitalized on prepandemic data of the neural connectome of fear to predict participants who would develop high fear-related symptoms in COVID-19 pandemic, suggesting that individual variations in the intrinsic organization of the fear circuits represent a neurofunctional marker that renders subjects vulnerable to experience high levels of fear during the COVID-19 pandemic.
Deep Learning Analysis in Prediction of COVID-19 Infection Status Using Chest CT Scan Features.
Authors: AsmaPourhoseingholi, MohsenVahedi, SamiraChaibakhsh, Mohamad AminPourhoseingholi, AmirVahedian-Azimi, Paul CGuest, FarshidRahimi-Bashar, AmirhosseinSahebkar
Journal: Advances in experimental medicine and biology

Background and aims Non-contrast chest computed tomography (CT) scanning is one of the important tools for evaluating of lung lesions. The aim of this study was to use a deep learning approach for predicting the outcome of patients with COVID-19 into two groups of critical and non-critical according to their CT features. Methods This was carried out as a retrospective study from March to April 2020 in Baqiyatallah Hospital, Tehran, Iran. From total of 1078 patients with COVID-19 pneumonia who underwent chest CT, 169 were critical cases and 909 were non-critical. Deep learning neural networks were used to classify samples into critical or non-critical ones according to the chest CT results. Results The best accuracy of prediction was seen by the presence of diffuse opacities and lesion distribution (both=0.91, 95% CI: 0.83-0.99). The largest sensitivity was achieved using lesion distribution (0.74, 95% CI: 0.55-0.93), and the largest specificity was for presence of diffuse opacities (0.95, 95% CI: 0.9-1). The total model showed an accuracy of 0.89 (95% CI: 0.79-0.99), and the corresponding sensitivity and specificity were 0.71 (95% CI: 0.51-0.91) and 0.93 (95% CI: 0.87-0.96), respectively. Conclusions The results showed that CT scan can accurately classify and predict critical and non-critical COVID-19 cases.
MIDCAN: A multiple input deep convolutional attention network for Covid-19 diagnosis based on chest CT and chest X-ray.
Authors: Yu-DongZhang, ZhengZhang, XinZhang, Shui-HuaWang
Journal: Pattern recognition letters

COVID-19 has caused 3.34m deaths till 13/May/2021. It is now still causing confirmed cases and ongoing deaths every day.
This study investigated whether fusing chest CT with chest X-ray can help improve the AI's diagnosis performance. Data harmonization is employed to make a homogeneous dataset. We create an end-to-end multiple-input deep convolutional attention network (MIDCAN) by using the convolutional block attention module (CBAM). One input of our model receives 3D chest CT image, and other input receives 2D X-ray image. Besides, multiple-way data augmentation is used to generate fake data on training set. Grad-CAM is used to give explainable heatmap.
The proposed MIDCAN achieves a sensitivity of 98.10±1.88%, a specificity of 97.95±2.26%, and an accuracy of 98.02±1.35%.
Our MIDCAN method provides better results than 8 state-of-the-art approaches. We demonstrate the using multiple modalities can achieve better results than individual modality. Also, we demonstrate that CBAM can help improve the diagnosis performance.
DeepEMhancer: a deep learning solution for cryo-EM volume post-processing.
Authors: RubenSanchez-Garcia, JosueGomez-Blanco, AnaCuervo, Jose MariaCarazo, Carlos Oscar SSorzano, JavierVargas
Journal: Communications biology

Cryo-EM maps are valuable sources of information for protein structure modeling. However, due to the loss of contrast at high frequencies, they generally need to be post-processed to improve their interpretability. Most popular approaches, based on global B-factor correction, suffer from limitations. For instance, they ignore the heterogeneity in the map local quality that reconstructions tend to exhibit. Aiming to overcome these problems, we present DeepEMhancer, a deep learning approach designed to perform automatic post-processing of cryo-EM maps. Trained on a dataset of pairs of experimental maps and maps sharpened using their respective atomic models, DeepEMhancer has learned how to post-process experimental maps performing masking-like and sharpening-like operations in a single step. DeepEMhancer was evaluated on a testing set of 20 different experimental maps, showing its ability to reduce noise levels and obtain more detailed versions of the experimental maps. Additionally, we illustrated the benefits of DeepEMhancer on the structure of the SARS-CoV-2 RNA polymerase.
Diagnosis of COVID-19 Using Machine Learning and Deep Learning: A Review.
Authors: M Rubaiyat HossainMondal, SubratoBharati, PrajoyPodder
Journal: Current medical imaging

This paper provides a systematic review of the application of Artificial Intelligence (AI) in the form of Machine Learning (ML) and Deep Learning (DL) techniques in fighting against the effects of novel coronavirus disease (COVID-19).
The objective is to perform a scoping review on AI for COVID-19 using preferred reporting items of systematic reviews and meta-analysis (PRISMA) guidelines. A literature search was performed for relevant studies published from 1 January 2020 till 27 March 2021. Out of 4050 research papers available in reputed publishers, a full-text review of 440 articles was done based on the keywords of AI, COVID-19, ML, forecasting, DL, X-ray, and Computed Tomography (CT). Finally, 52 articles were included in the result synthesis of this paper. As part of the review, different ML regression methods were reviewed first in predicting the number of confirmed and death cases. Secondly, a comprehensive survey was carried out on the use of ML in classifying COVID-19 patients. Thirdly, different datasets on medical imaging were compared in terms of the number of images, number of positive samples and number of classes in the datasets. The different stages of the diagnosis, including preprocessing, segmentation and feature extraction were also reviewed. Fourthly, the performance results of different research papers were compared to evaluate the effectiveness of DL methods on different datasets.
Results show that residual neural network (ResNet-18) and densely connected convolutional network (DenseNet 169) exhibit excellent classification accuracy for X-ray images, while DenseNet-201 has the maximum accuracy in classifying CT scan images. This indicates that ML and DL are useful tools in assisting researchers and medical professionals in predicting, screening and detecting COVID-19.
Finally, this review highlights the existing challenges, including regulations, noisy data, data privacy, and the lack of reliable large datasets, then provides future research directions in applying AI in managing COVID-19.
Deep learning for COVID-19 detection based on CT images.
Authors: WentaoZhao, WeiJiang, XinguoQiu
Journal: Scientific reports

COVID-19 has tremendously impacted patients and medical systems globally. Computed tomography images can effectively complement the reverse transcription-polymerase chain reaction testing. This study adopted a convolutional neural network for COVID-19 testing. We examined the performance of different pre-trained models on CT testing and identified that larger, out-of-field datasets boost the testing power of the models. This suggests that a priori knowledge of the models from out-of-field training is also applicable to CT images. The proposed transfer learning approach proves to be more successful than the current approaches described in literature. We believe that our approach has achieved the state-of-the-art performance in identification thus far. Based on experiments with randomly sampled training datasets, the results reveal a satisfactory performance by our model. We investigated the relevant visual characteristics of the CT images used by the model; these may assist clinical doctors in manual screening.
Comparison of deep learning, radiomics and subjective assessment of chest CT findings in SARS-CoV-2 pneumonia.
Authors: ChiaraArru, ShadiEbrahimian, ZenoFalaschi, Jacob ValentinHansen, AlessioPasche, Mads DamLyhne, MathisZimmermann, FelixDurlak, MatthiasMitschke, AlessandroCarriero, Jens ErikNielsen-Kudsk, Mannudeep KKalra, LucaSaba
Journal: Clinical imaging

Comparison of deep learning algorithm, radiomics and subjective assessment of chest CT for predicting outcome (death or recovery) and intensive care unit (ICU) admission in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.
The multicenter, ethical committee-approved, retrospective study included non-contrast-enhanced chest CT of 221 SARS-CoV-2 positive patients from Italy (n = 196 patients; mean age 64 ± 16 years) and Denmark (n = 25; mean age 69 ± 13 years). A thoracic radiologist graded presence, type and extent of pulmonary opacities and severity of motion artifacts in each lung lobe on all chest CTs. Thin-section CT images were processed with CT Pneumonia Analysis Prototype (Siemens Healthineers) which yielded segmentation masks from a deep learning (DL) algorithm to derive features of lung abnormalities such as opacity scores, mean HU, as well as volume and percentage of all-attenuation and high-attenuation (opacities >-200 HU) opacities. Separately, whole lung radiomics were obtained for all CT exams. Analysis of variance and multiple logistic regression were performed for data analysis.
Moderate to severe respiratory motion artifacts affected nearly one-quarter of chest CTs in patients. Subjective severity assessment, DL-based features and radiomics predicted patient outcome (AUC 0.76 vs AUC 0.88 vs AUC 0.83) and need for ICU admission (AUC 0.77 vs AUC 0.0.80 vs 0.82). Excluding chest CT with motion artifacts, the performance of DL-based and radiomics features improve for predicting ICU admission.
DL-based and radiomics features of pulmonary opacities from chest CT were superior to subjective assessment for differentiating patients with favorable and adverse outcomes.
Deep learning applied to lung ultrasound videos for scoring COVID-19 patients: A multicenter study.
Authors: FedericoMento, TizianoPerrone, AnnaFiengo, AndreaSmargiassi, RiccardoInchingolo, GinoSoldati, LibertarioDemi
Journal: The Journal of the Acoustical Society of America

In the current pandemic, lung ultrasound (LUS) played a useful role in evaluating patients affected by COVID-19. However, LUS remains limited to the visual inspection of ultrasound data, thus negatively affecting the reliability and reproducibility of the findings. Moreover, many different imaging protocols have been proposed, most of which lacked proper clinical validation. To address these problems, we were the first to propose a standardized imaging protocol and scoring system. Next, we developed the first deep learning (DL) algorithms capable of evaluating LUS videos providing, for each video-frame, the score as well as semantic segmentation. Moreover, we have analyzed the impact of different imaging protocols and demonstrated the prognostic value of our approach. In this work, we report on the level of agreement between the DL and LUS experts, when evaluating LUS data. The results show a percentage of agreement between DL and LUS experts of 85.96% in the stratification between patients at high risk of clinical worsening and patients at low risk. These encouraging results demonstrate the potential of DL models for the automatic scoring of LUS data, when applied to high quality data acquired accordingly to a standardized imaging protocol.
Fuzzy rank-based fusion of CNN models using Gompertz function for screening COVID-19 CT-scans.
Authors: RohitKundu, HritamBasak, Pawan KumarSingh, AliAhmadian, MassimilianoFerrara, RamSarkar
Journal: Scientific reports

COVID-19 has crippled the world's healthcare systems, setting back the economy and taking the lives of several people. Although potential vaccines are being tested and supplied around the world, it will take a long time to reach every human being, more so with new variants of the virus emerging, enforcing a lockdown-like situation on parts of the world. Thus, there is a dire need for early and accurate detection of COVID-19 to prevent the spread of the disease, even more. The current gold-standard RT-PCR test is only 71% sensitive and is a laborious test to perform, leading to the incapability of conducting the population-wide screening. To this end, in this paper, we propose an automated COVID-19 detection system that uses CT-scan images of the lungs for classifying the same into COVID and Non-COVID cases. The proposed method applies an ensemble strategy that generates fuzzy ranks of the base classification models using the Gompertz function and fuses the decision scores of the base models adaptively to make the final predictions on the test cases. Three transfer learning-based convolutional neural network models are used, namely VGG-11, Wide ResNet-50-2, and Inception v3, to generate the decision scores to be fused by the proposed ensemble model. The framework has been evaluated on two publicly available chest CT scan datasets achieving state-of-the-art performance, justifying the reliability of the model. The relevant source codes related to the present work is available in: GitHub.
COVID-19 lesion detection and segmentation-A deep learning method.
Authors: LiuJingxin, ZhangMengchao, LiuYuchen, CuiJinglei, ZhongYutong, ZhangZhong, ZuLihui
Journal: Methods (San Diego, Calif.)

In this paper, we utilized deep learning methods to screen the positive COVID-19 cases in chest CT. Our primary goal is to supply rapid and precise assistance for disease surveillance on the medical imaging aspect.
Basing on deep learning, we combined semantic segmentation and object detection methods to study the lesion performance of COVID-19. We put forward a novel end-to-end model which takes advantage of the Spatio-temporal features. Furthermore, a segmentation model attached with a fully connected CRF was designed for a more effective ROI input.
Our method showed a better performance across different metrics against the comparison models. Moreover, our strategy highlighted strong robustness for the processed augmented testing samples.
The comprehensive fusion of Spatio-temporal correlations can exploit more valuable features for locating target regions, and this mechanism is friendly to detect tiny lesions. Although it remains in discrete form, the feature extracting in temporal dimension improves the precision of final prediction.
Artificial intelligence for prediction of COVID-19 progression using CT imaging and clinical data.
Authors: RobinWang, ZhichengJiao, LiYang, Ji WhaeChoi, ZengXiong, KaseyHalsey, Thi My LinhTran, IanPan, Scott ACollins, XueFeng, JingWu, KenChang, Lin-BoShi, ShuaiYang, Qi-ZhiYu, JieLiu, Fei-XianFu, Xiao-LongJiang, Dong-CuiWang, Li-PingZhu, Xiao-PingYi, Terrance THealey, Qiu-HuaZeng, TaoLiu, Ping-FengHu, Raymond YHuang, Yi-HuiLi, Ronnie ASebro, Paul J LZhang, JianxinWang, Michael KAtalay, Wei-HuaLiao, YongFan, Harrison XBai
Journal: European radiology

Early recognition of coronavirus disease 2019 (COVID-19) severity can guide patient management. However, it is challenging to predict when COVID-19 patients will progress to critical illness. This study aimed to develop an artificial intelligence system to predict future deterioration to critical illness in COVID-19 patients.
An artificial intelligence (AI) system in a time-to-event analysis framework was developed to integrate chest CT and clinical data for risk prediction of future deterioration to critical illness in patients with COVID-19.
A multi-institutional international cohort of 1,051 patients with RT-PCR confirmed COVID-19 and chest CT was included in this study. Of them, 282 patients developed critical illness, which was defined as requiring ICU admission and/or mechanical ventilation and/or reaching death during their hospital stay. The AI system achieved a C-index of 0.80 for predicting individual COVID-19 patients' to critical illness. The AI system successfully stratified the patients into high-risk and low-risk groups with distinct progression risks (p < 0.0001).
Using CT imaging and clinical data, the AI system successfully predicted time to critical illness for individual patients and identified patients with high risk. AI has the potential to accurately triage patients and facilitate personalized treatment.
• AI system can predict time to critical illness for patients with COVID-19 by using CT imaging and clinical data.
Images denoising for COVID-19 chest X-ray based on multi-resolution parallel residual CNN.
Authors: XiaobenJiang, YuZhu, BingbingZheng, DaweiYang
Journal: Machine vision and applications

Chest X-ray (CXR) is a medical imaging technology that is common and economical to use in clinical. Recently, coronavirus (COVID-19) has spread worldwide, and the second wave is rebounding strongly now with the coming winter that has a detrimental effect on the global economy and health. To make pre-diagnosis of COVID-19 as soon as possible, and reduce the work pressure of medical staff, making use of deep learning networks to detect positive CXR images of infected patients is a critical step. However, there are complex edge structures and rich texture details in the CXR images susceptible to noise that can interfere with the diagnosis of the machines and the doctors. Therefore, in this paper, we proposed a novel multi-resolution parallel residual CNN (named MPR-CNN) for CXR images denoising and special application for COVID-19 which can improve the image quality. The core of MPR-CNN consists of several essential modules. (a) Multi-resolution parallel convolution streams are utilized for extracting more reliable spatial and semantic information in multi-scale features. (b) Efficient channel and spatial attention can let the network focus more on texture details in CXR images with fewer parameters. (c) The adaptive multi-resolution feature fusion method based on attention is utilized to improve the expression of the network. On the whole, MPR-CNN can simultaneously retain spatial information in the shallow layers with high resolution and semantic information in the deep layers with low resolution. Comprehensive experiments demonstrate that our MPR-CNN can better retain the texture structure details in CXR images. Additionally, extensive experiments show that our MPR-CNN has a positive impact on CXR images classification and detection of COVID-19 cases from denoised CXR images.
Deep convolution neural networks to differentiate between COVID-19 and other pulmonary abnormalities on chest radiographs: Evaluation using internal and external datasets.
Authors: YongwonCho, Sung HoHwang, Yu-WhanOh, Byung-JooHam, Min JuKim, Beom JinPark
Journal: International journal of imaging systems and technology

We aimed to evaluate the performance of convolutional neural networks (CNNs) in the classification of coronavirus disease 2019 (COVID-19) disease using normal, pneumonia, and COVID-19 chest radiographs (CXRs). First, we collected 9194 CXRs from open datasets and 58 from the Korea University Anam Hospital (KUAH). The number of normal, pneumonia, and COVID-19 CXRs were 4580, 3884, and 730, respectively. The CXRs obtained from the open dataset were randomly assigned to the training, tuning, and test sets in a 70:10:20 ratio. For external validation, the KUAH (20 normal, 20 pneumonia, and 18 COVID-19) dataset, verified by radiologists using computed tomography, was used. Subsequently, transfer learning was conducted using DenseNet169, InceptionResNetV2, and Xception to identify COVID-19 using open datasets (internal) and the KUAH dataset (external) with histogram matching. Gradient-weighted class activation mapping was used for the visualization of abnormal patterns in CXRs. The average AUC and accuracy of the multiscale and mixed-COVID-19Net using three CNNs over five folds were (0.99 ± 0.01 and 92.94% ± 0.45%), (0.99 ± 0.01 and 93.12% ± 0.23%), and (0.99 ± 0.01 and 93.57% ± 0.29%), respectively, using the open datasets (internal). Furthermore, these values were (0.75 and 74.14%), (0.72 and 68.97%), and (0.77 and 68.97%), respectively, for the best model among the fivefold cross-validation with the KUAH dataset (external) using domain adaptation. The various state-of-the-art models trained on open datasets show satisfactory performance for clinical interpretation. Furthermore, the domain adaptation for external datasets was found to be important for detecting COVID-19 as well as other diseases.