pt-sk/ml_research_abs huggingface dataset:
Segmentation of infected region in CT images of COVID-19 patients based on QC-HC U-net.
Authors: QinZhang, XiaoqiangRen, BenzhengWei
Journal: Scientific reports

Since the outbreak of COVID-19 in 2019, the rapid spread of the epidemic has brought huge challenges to medical institutions. If the pathological region in the COVID-19 CT image can be automatically segmented, it will help doctors quickly determine the patient's infection, thereby speeding up the diagnosis process. To be able to automatically segment the infected area, we proposed a new network structure and named QC-HC U-Net. First, we combine residual connection and dense connection to form a new connection method and apply it to the encoder and the decoder. Second, we choose to add Hypercolumns in the decoder section. Compared with the benchmark 3D U-Net, the improved network can effectively avoid vanishing gradient while extracting more features. To improve the situation of insufficient data, resampling and data enhancement methods are selected in this paper to expand the datasets. We used 63 cases of MSD lung tumor data for training and testing, continuously verified to ensure the training effect of this model, and then selected 20 cases of public COVID-19 data for training and testing. Experimental results showed that in the segmentation of COVID-19, the specificity and sensitivity were 85.3% and 83.6%, respectively, and in the segmentation of MSD lung tumors, the specificity and sensitivity were 81.45% and 80.93%, respectively, without any fitting.
Factors determining generalization in deep learning models for scoring COVID-CT images.
Authors: Michael JamesHorry, SubrataChakraborty, BiswajeetPradhan, MaryamFallahpoor, HosseinChegeni, ManoranjanPaul
Journal: Mathematical biosciences and engineering : MBE

The COVID-19 pandemic has inspired unprecedented data collection and computer vision modelling efforts worldwide, focused on the diagnosis of COVID-19 from medical images. However, these models have found limited, if any, clinical application due in part to unproven generalization to data sets beyond their source training corpus. This study investigates the generalizability of deep learning models using publicly available COVID-19 Computed Tomography data through cross dataset validation. The predictive ability of these models for COVID-19 severity is assessed using an independent dataset that is stratified for COVID-19 lung involvement. Each inter-dataset study is performed using histogram equalization, and contrast limited adaptive histogram equalization with and without a learning Gabor filter. We show that under certain conditions, deep learning models can generalize well to an external dataset with F1 scores up to 86%. The best performing model shows predictive accuracy of between 75% and 96% for lung involvement scoring against an external expertly stratified dataset. From these results we identify key factors promoting deep learning generalization, being primarily the uniform acquisition of training images, and secondly diversity in CT slice position.
Drr4covid: Learning Automated COVID-19 Infection Segmentation From Digitally Reconstructed Radiographs.
Authors: PengyiZhang, YunxinZhong, YulinDeng, XiaoyingTang, XiaoqiongLi
Journal: IEEE access : practical innovations, open solutions

Automated infection measurement and COVID-19 diagnosis based on Chest X-ray (CXR) imaging is important for faster examination, where infection segmentation is an essential step for assessment and quantification. However, due to the heterogeneity of X-ray imaging and the difficulty of annotating infected regions precisely, learning automated infection segmentation on CXRs remains a challenging task. We propose a novel approach, called DRR4Covid, to learn COVID-19 infection segmentation on CXRs from digitally reconstructed radiographs (DRRs). DRR4Covid consists of an infection-aware DRR generator, a segmentation network, and a domain adaptation module. Given a labeled Computed Tomography scan, the infection-aware DRR generator can produce infection-aware DRRs with pixel-level annotations of infected regions for training the segmentation network. The domain adaptation module is designed to enable the segmentation network trained on DRRs to generalize to CXRs. The statistical analyses made on experiment results have indicated that our infection-aware DRRs are significantly better than standard DRRs in learning COVID-19 infection segmentation (p < 0.05) and the domain adaptation module can improve the infection segmentation performance on CXRs significantly (p < 0.05). Without using any annotations of CXRs, our network has achieved a classification score of (Accuracy: 0.949, AUC: 0.987, F1-score: 0.947) and a segmentation score of (Accuracy: 0.956, AUC: 0.980, F1-score: 0.955) on a test set with 558 normal cases and 558 positive cases. Besides, by adjusting the strength of radiological signs of COVID-19 infection in infection-aware DRRs, we estimate the detection limit of X-ray imaging in detecting COVID-19 infection. The estimated detection limit, measured by the percent volume of the lung that is infected by COVID-19, is 19.43% ± 16.29%, and the estimated lower bound of infected voxel contribution rate for significant radiological signs of COVID-19 infection is 20.0%. Our codes are made publicly available at https://github.com/PengyiZhang/DRR4Covid.
2019 Novel Coronavirus-Infected Pneumonia on CT: A Feasibility Study of Few-Shot Learning for Computerized Diagnosis of Emergency Diseases.
Authors: YaomingLai, GuangmingLi, DongmeiWu, WanminLian, ChengLi, JunzhangTian, XiaofenMa, HuiChen, WenXu, JunWei, YaqinZhang, GuihuaJiang
Journal: IEEE access : practical innovations, open solutions

COVID-19 is an emerging disease with transmissibility and severity. So far, there are no effective therapeutic drugs or vaccines for COVID-19. The most serious complication of COVID-19 is a type of pneumonia called 2019 novel coronavirus-infected pneumonia (NCIP) with about 4.3% mortality rate. Comparing to chest Digital Radiography (DR), it is recently reported that chest Computed Tomography (CT) is more useful to serve as the early screening and diagnosis tool for NCIP. In this study, aimed to help physicians make the diagnostic decision, we develop a machine learning (ML) approach for automated diagnosis of NCIP on chest CT. Different from most ML approaches which often require training on thousands or millions of samples, we design a few-shot learning approach, in which we combine few-shot learning with weakly supervised model training, for computerized NCIP diagnosis. A total of 824 patients are retrospectively collected from two Hospitals with IRB approval. We first use 9 patients with clinically confirmed NCIP and 20 patients without known lung diseases for training a location detector which is a multitask deep convolutional neural network (DCNN) designed to output a probability of NCIP and the segmentation of targeted lesion area. An experienced radiologist manually localizes the potential locations of NCIPs on chest CTs of 9 COVID-19 patients and interactively segments the area of the NCIP lesions as the reference standard. Then, the multitask DCNN is furtherly fine-tuned by a weakly supervised learning scheme with 291 case-level labeled samples without lesion labels. A test set of 293 patients is independently collected for evaluation. With our NCIP-Net, the test AUC is 0.91. Our system has potential to serve as the NCIP screening and diagnosis tools for the fight of COVID-19's endemic and pandemic.
UMLF-COVID: an unsupervised meta-learning model specifically designed to identify X-ray images of COVID-19 patients.
Authors: RuiMiao, XinDong, Sheng-LiXie, YongLiang, Sio-LongLo
Journal: BMC medical imaging

With the rapid spread of COVID-19 worldwide, quick screening for possible COVID-19 patients has become the focus of international researchers. Recently, many deep learning-based Computed Tomography (CT) image/X-ray image fast screening models for potential COVID-19 patients have been proposed. However, the existing models still have two main problems. First, most of the existing supervised models are based on pre-trained model parameters. The pre-training model needs to be constructed on a dataset with features similar to those in COVID-19 X-ray images, which limits the construction and use of the model. Second, the number of categories based on the X-ray dataset of COVID-19 and other pneumonia patients is usually imbalanced. In addition, the quality is difficult to distinguish, leading to non-ideal results with the existing model in the multi-class classification COVID-19 recognition task. Moreover, no researchers have proposed a COVID-19 X-ray image learning model based on unsupervised meta-learning.
This paper first constructed an unsupervised meta-learning model for fast screening of COVID-19 patients (UMLF-COVID). This model does not require a pre-trained model, which solves the limitation problem of model construction, and the proposed unsupervised meta-learning framework solves the problem of sample imbalance and sample quality.
The UMLF-COVID model is tested on two real datasets, each of which builds a three-category and four-category model. And the experimental results show that the accuracy of the UMLF-COVID model is 3-10% higher than that of the existing models.
In summary, we believe that the UMLF-COVID model is a good complement to COVID-19 X-ray fast screening models.
A multitask dual-stream attention network for the identification of KRAS mutation in colorectal cancer.
Authors: KaiSong, ZijuanZhao, YulanMa, JiaWenWang, WeiWu, YanQiang, JuanjuanZhao, SumanChaudhary
Journal: Medical physics

It is of great significance to accurately identify the KRAS gene mutation status for patients in tumor prognosis and personalized treatment. Although the computer-aided diagnosis system based on deep learning has gotten all-round development, its performance still cannot meet the current clinical application requirements due to the inherent limitations of small-scale medical image data set and inaccurate lesion feature extraction. Therefore, our aim is to propose a deep learning model based on T2 MRI of colorectal cancer (CRC) patients to identify whether KRAS gene is mutated.
In this research, a multitask attentive model is proposed to identify KRAS gene mutations in patients, which is mainly composed of a segmentation subnetwork and an identification subnetwork. Specifically, at first, the features extracted by the encoder of segmentation model are used as guidance information to guide the two attention modules in the identification network for precise activation of the lesion area. Then the original image of the lesion and the segmentation result are concatenated for feature extraction. Finally, features extracted from the second step are combined with features activated by the attention modules to identify the gene mutation status. In this process, we introduce the interlayer loss function to encourage the similarity of the two subnetwork parameters and ensure that the key features are fully extracted to alleviate the overfitting problem caused by small data set to some extent.
The proposed identification model is benchmarked primarily using 15-fold cross validation. Three hundred and eighty-two images from 36 clinical cases were used to test the model. For the identification of KRAS mutation status, the average accuracy is 89.95 
We developed a novel deep learning-based model to identify the KRAS status in CRC. We demonstrated the excellent properties of the proposed identification through comparison with ground truth gene mutation status of 36 clinical cases. And all these results show that the novel method has great potential for clinical application.
A review of explainable and interpretable AI with applications in COVID-19 imaging.
Authors: Jordan DFuhrman, NaveenaGorre, QiyuanHu, HuiLi, IssamEl Naqa, Maryellen LGiger
Journal: Medical physics

The development of medical imaging artificial intelligence (AI) systems for evaluating COVID-19 patients has demonstrated potential for improving clinical decision making and assessing patient outcomes during the recent COVID-19 pandemic. These have been applied to many medical imaging tasks, including disease diagnosis and patient prognosis, as well as augmented other clinical measurements to better inform treatment decisions. Because these systems are used in life-or-death decisions, clinical implementation relies on user trust in the AI output. This has caused many developers to utilize explainability techniques in an attempt to help a user understand when an AI algorithm is likely to succeed as well as which cases may be problematic for automatic assessment, thus increasing the potential for rapid clinical translation. AI application to COVID-19 has been marred with controversy recently. This review discusses several aspects of explainable and interpretable AI as it pertains to the evaluation of COVID-19 disease and it can restore trust in AI application to this disease. This includes the identification of common tasks that are relevant to explainable medical imaging AI, an overview of several modern approaches for producing explainable output as appropriate for a given imaging scenario, a discussion of how to evaluate explainable AI, and recommendations for best practices in explainable/interpretable AI implementation. This review will allow developers of AI systems for COVID-19 to quickly understand the basics of several explainable AI techniques and assist in the selection of an approach that is both appropriate and effective for a given scenario.
Deep Learning Algorithm for COVID-19 Classification Using Chest X-Ray Images.
Authors: SharmilaV J, Jemi FlorinabelD
Journal: Computational and mathematical methods in medicine

Early diagnosis of the harmful severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), along with clinical expertise, allows governments to break the transition chain and flatten the epidemic curve. Although reverse transcription-polymerase chain reaction (RT-PCR) offers quick results, chest X-ray (CXR) imaging is a more reliable method for disease classification and assessment. The rapid spread of the coronavirus disease 2019 (COVID-19) has triggered extensive research towards developing a COVID-19 detection toolkit. Recent studies have confirmed that the deep learning-based approach, such as convolutional neural networks (CNNs), provides an optimized solution for COVID-19 classification; however, they require substantial training data for learning features. Gathering this training data in a short period has been challenging during the pandemic. Therefore, this study proposes a new model of CNN and deep convolutional generative adversarial networks (DCGANs) that classify CXR images into normal, pneumonia, and COVID-19. The proposed model contains eight convolutional layers, four max-pooling layers, and two fully connected layers, which provide better results than the existing pretrained methods (AlexNet and GoogLeNet). DCGAN performs two tasks: (1) generating synthetic/fake images to overcome the challenges of an imbalanced dataset and (2) extracting deep features of all images in the dataset. In addition, it enlarges the dataset and represents the characteristics of diversity to provide a good generalization effect. In the experimental analysis, we used four distinct publicly accessible datasets of chest X-ray images (COVID-19 X-ray, COVID Chest X-ray, COVID-19 Radiography, and CoronaHack-Chest X-Ray) to train and test the proposed CNN and the existing pretrained methods. Thereafter, the proposed CNN method was trained with the four datasets based on the DCGAN synthetic images, resulting in higher accuracy (94.8%, 96.6%, 98.5%, and 98.6%) than the existing pretrained models. The overall results suggest that the proposed DCGAN-CNN approach is a promising solution for efficient COVID-19 diagnosis.
Deep Learning Approaches for Detecting COVID-19 From Chest X-Ray Images: A Survey.
Authors: Hanan SAlghamdi, GhadaAmoudi, SalmaElhag, KawtherSaeedi, JomanahNasser
Journal: IEEE access : practical innovations, open solutions

Chest X-ray (CXR) imaging is a standard and crucial examination method used for suspected cases of coronavirus disease (COVID-19). In profoundly affected or limited resource areas, CXR imaging is preferable owing to its availability, low cost, and rapid results. However, given the rapidly spreading nature of COVID-19, such tests could limit the efficiency of pandemic control and prevention. In response to this issue, artificial intelligence methods such as deep learning are promising options for automatic diagnosis because they have achieved state-of-the-art performance in the analysis of visual information and a wide range of medical images. This paper reviews and critically assesses the preprint and published reports between March and May 2020 for the diagnosis of COVID-19 via CXR images using convolutional neural networks and other deep learning architectures. Despite the encouraging results, there is an urgent need for public, comprehensive, and diverse datasets. Further investigations in terms of explainable and justifiable decisions are also required for more robust, transparent, and accurate predictions.
Deep Convolutional Approaches for the Analysis of COVID-19 Using Chest X-Ray Images From Portable Devices.
Authors: JoaquimDe Moura, Lucia RamosGarcia, Placido Francisco LizancosVidal, MilenaCruz, Laura AbelairasLopez, Eva CastroLopez, JorgeNovo, MarcosOrtega
Journal: IEEE access : practical innovations, open solutions

The recent human coronavirus disease (COVID-19) is a respiratory infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Given the effects of COVID-19 in pulmonary tissues, chest radiography imaging plays an important role in the screening, early detection, and monitoring of the suspected individuals. Hence, as the pandemic of COVID-19 progresses, there will be a greater reliance on the use of portable equipment for the acquisition of chest X-ray images due to its accessibility, widespread availability, and benefits regarding to infection control issues, minimizing the risk of cross-contamination. This work presents novel fully automatic approaches specifically tailored for the classification of chest X-ray images acquired by portable equipment into 3 different clinical categories: normal, pathological, and COVID-19. For this purpose, 3 complementary deep learning approaches based on a densely convolutional network architecture are herein presented. The joint response of all the approaches allows to enhance the differentiation between patients infected with COVID-19, patients with other diseases that manifest characteristics similar to COVID-19 and normal cases. The proposed approaches were validated over a dataset specifically retrieved for this research. Despite the poor quality of the chest X-ray images that is inherent to the nature of the portable equipment, the proposed approaches provided global accuracy values of 79.62%, 90.27% and 79.86%, respectively, allowing a reliable analysis of portable radiographs to facilitate the clinical decision-making process.
A deep learning approach using effective preprocessing techniques to detect COVID-19 from chest CT-scan and X-ray images.
Authors: Khabir UddinAhamed, ManowarulIslam, AshrafUddin, ArnishaAkhter, Bikash KumarPaul, Mohammad AbuYousuf, ShahadatUddin, Julian M WQuinn, Mohammad AliMoni
Journal: Computers in biology and medicine

Coronavirus disease-19 (COVID-19) is a severe respiratory viral disease first reported in late 2019 that has spread worldwide. Although some wealthy countries have made significant progress in detecting and containing this disease, most underdeveloped countries are still struggling to identify COVID-19 cases in large populations. With the rising number of COVID-19 cases, there are often insufficient COVID-19 diagnostic kits and related resources in such countries. However, other basic diagnostic resources often do exist, which motivated us to develop Deep Learning models to assist clinicians and radiologists to provide prompt diagnostic support to the patients. In this study, we have developed a deep learning-based COVID-19 case detection model trained with a dataset consisting of chest CT scans and X-ray images. A modified ResNet50V2 architecture was employed as deep learning architecture in the proposed model. The dataset utilized to train the model was collected from various publicly available sources and included four class labels: confirmed COVID-19, normal controls and confirmed viral and bacterial pneumonia cases. The aggregated dataset was preprocessed through a sharpening filter before feeding the dataset into the proposed model. This model attained an accuracy of 96.452% for four-class cases (COVID-19/Normal/Bacterial pneumonia/Viral pneumonia), 97.242% for three-class cases (COVID-19/Normal/Bacterial pneumonia) and 98.954% for two-class cases (COVID-19/Viral pneumonia) using chest X-ray images. The model acquired a comprehensive accuracy of 99.012% for three-class cases (COVID-19/Normal/Community-acquired pneumonia) and 99.99% for two-class cases (Normal/COVID-19) using CT-scan images of the chest. This high accuracy presents a new and potentially important resource to enable radiologists to identify and rapidly diagnose COVID-19 cases with only basic but widely available equipment.
Potential diagnosis of COVID-19 from chest X-ray and CT findings using semi-supervised learning.
Authors: PrachetaSahoo, IndranilRoy, RandeepAhlawat, SaquibIrtiza, LatifurKhan
Journal: Physical and engineering sciences in medicine

COVID-19 is an infectious disease, which has adversely affected public health and the economy across the world. On account of the highly infectious nature of the disease, rapid automated diagnosis of COVID-19 is urgently needed. A few recent findings suggest that chest X-rays and CT scans can be used by machine learning for the diagnosis of COVID-19. Herein, we employed semi-supervised learning (SSL) approaches to detect COVID-19 cases accurately by analyzing digital chest X-rays and CT scans. On a relatively small COVID-19 radiography dataset, which contains only 219 COVID-19 positive images, 1341 normal and 1345 viral pneumonia images, our algorithm, COVIDCon, which takes advantage of data augmentation, consistency regularization, and multicontrastive learning, attains 97.07% average class prediction accuracy, with 1000 labeled images, which is 7.65% better than the next best SSL method, virtual adversarial training. COVIDCon performs even better on a larger COVID-19 CT Scan dataset that contains 82,767 images. It achieved an excellent accuracy of 99.13%, at 20,000 labels, which is 6.45% better than the next best pseudo-labeling approach. COVIDCon outperforms other state-of-the-art algorithms at every label that we have investigated. These results demonstrate COVIDCon as the benchmark SSL algorithm for potential diagnosis of COVID-19 from chest X-rays and CT-Scans. Furthermore, COVIDCon performs exceptionally well in identifying COVID-19 positive cases from a completely unseen repository with a confirmed COVID-19 case history. COVIDCon, may provide a fast, accurate, and reliable method for screening COVID-19 patients.