LitCovid-sentences  

PMC:7782580 / 30390-42105 JSONTXT 3 Projects

Annnotations TAB TSV DIC JSON TextAE

Id Subject Object Predicate Lexical cue
T233 0-10 Sentence denotes Discussion
T234 11-131 Sentence denotes We developed a computer-aided diagnosis method for the identification of COVID-19 in medical images using DL techniques.
T235 132-257 Sentence denotes Strong correlations were obtained between the lesion areas identified by the proposed CNNRF and the five clinical indicators.
T236 258-339 Sentence denotes An excellent agreement was observed between the model results and expert opinion.
T237 340-624 Sentence denotes Popular image annotation tools (e.g., Labelme46 and VOTT47) are used to annotate various images and support common formats, such as Joint Photographic Experts Group (JPG), Portable Network Graphics (PNG), and Tag Image File Format (TIFF); these formats are not used in the DICOM data.
T238 625-802 Sentence denotes Therefore, we developed an annotation platform that does not require much storage space or transformations and can be deployed on a private cloud for security and local sharing.
T239 803-952 Sentence denotes Our eyes are not highly sensitive to grayscale images in regions with high average brightness48, resulting in relatively low identification accuracy.
T240 953-1092 Sentence denotes The proposed pseudo-color method increased the information content of the medical images and facilitated the identification of the details.
T241 1093-1192 Sentence denotes PCA has been widely used for feature extraction and dimensionality reduction in image processing49.
T242 1193-1257 Sentence denotes We used PCA to determine the feature space of the sub-data sets.
T243 1258-1357 Sentence denotes Each image in a specified sub-data set was represented as a linear combination of the eigenvectors.
T244 1358-1475 Sentence denotes Since the eigenvectors describe the most informative regions in the medical images, they represent each sub-data set.
T245 1476-1563 Sentence denotes We visualized the top-five eigenvectors of each sub-data set using an intuitive method.
T246 1564-1715 Sentence denotes The CNNCF is a modular framework consisting of two stages that were trained with different optimization goals and controlled by the control gate block.
T247 1716-1893 Sentence denotes Each stage consisted of multiple residual blocks (ResBlock-A and ResBlock-B) that retained the features in the different layers, thereby preventing the degradation of the model.
T248 1894-2001 Sentence denotes The design of the control gate block was inspired by the synaptic frontend structure in the nervous system.
T249 2002-2110 Sentence denotes We calculated the score of the optimization target, and a score above a predefined threshold was acceptable.
T250 2111-2254 Sentence denotes If the times of the neurotransmitter were above another predefined threshold, the control gate was opened to let the features information pass.
T251 2255-2306 Sentence denotes The framework was trained in a step-by-step manner.
T252 2307-2516 Sentence denotes Training occurred at each stage for a specified goal, and the second stage used the features extracted by the first stage, thereby reusing the features and increasing the convergence speed of the second stage.
T253 2517-2634 Sentence denotes The CNNCF exhibited excellent performance for identifying the COVID-19 cases automatically in the X-data and CT-data.
T254 2635-2822 Sentence denotes Unlike traditional machine learning methods, the CNNCF was trained in an end-to-end manner, which ensured the flexibility of the framework for different data sets without much adjustment.
T255 2823-3056 Sentence denotes We adopted a knowledge distillation method in the training phrase; a small model (called a student network) was trained to mimic the ensemble of multiple models (called teacher networks) to obtain a small model with high performance.
T256 3057-3188 Sentence denotes In the distillation process, knowledge was transferred from the teacher networks to the student network to minimize knowledge loss.
T257 3189-3278 Sentence denotes The target was the output of the teacher networks; these outputs were called soft labels.
T258 3279-3468 Sentence denotes The student network also learned from the ground-truth labels (also called hard labels), thereby minimizing the knowledge loss from the student networks, whose targets were the hard labels.
T259 3469-3615 Sentence denotes Therefore, the overall loss function of the student network incorporated both knowledge distillation and knowledge loss from the student networks.
T260 3616-3859 Sentence denotes After the student network had been well-trained, the task of the teacher networks was complete, and the student model could be used on a regular computer with a fast speed, which is suitable for hospitals without extensive computing resources.
T261 3860-3991 Sentence denotes As a result of the knowledge distillation method, the CNNCF achieved high performance with a few parameters in the teacher network.
T262 3992-4120 Sentence denotes The CNNRF is a modular framework consisting of one stage II sub-framework and one regressor block to handle the regression task.
T263 4121-4392 Sentence denotes In the regressor block, we used skip connections that consisted of a convolution layer with multiple 1 × 1 convolution kernels for retaining the features extracted by the stage II sub-framework while improving the non-linear representation ability of the regressor block.
T264 4393-4592 Sentence denotes We made use of flexible blocks to achieve good performance for the classification and regression tasks, unlike traditional machine learning methods, which are commonly used for either of these tasks.
T265 4593-4743 Sentence denotes Five statistical indices, including sensitivity, specificity, precision, kappa coefficient, and F1 were used to evaluate the performance of the CNNCF.
T266 4744-4869 Sentence denotes The sensitivity is related to the positive detection rate and is of great significance in the diagnostic testing of COVID-19.
T267 4870-4969 Sentence denotes The specificity refers to the ability of the model to correctly identify patients with the disease.
T268 4970-5052 Sentence denotes The precision indicates the ability of the model to provide a positive prediction.
T269 5053-5116 Sentence denotes The kappa demonstrates the stability of the model’s prediction.
T270 5117-5174 Sentence denotes The F1 is the harmonic mean of precision and sensitivity.
T271 5175-5313 Sentence denotes Good performance was achieved by the CNNCF based on the five statistical indices for the multi-modal image data sets (X-data and CT-data).
T272 5314-5418 Sentence denotes The consistency between the model results and the expert evaluation was determined using McNemar’s test.
T273 5419-5594 Sentence denotes The good performance demonstrated the model’s capacity of learning from the experts using the labels of the image data and mimicking the experts in diagnostic decision-making.
T274 5595-5692 Sentence denotes The ROC and PRC of the CNNCF were used to evaluate the performance of the classification model50.
T275 5693-5851 Sentence denotes The ROC is a probability curve that shows the trade-off between the true positive rate (TPR) and false-positive rate (FPR) using different threshold settings.
T276 5852-5970 Sentence denotes The AUROC provides a measure of separability and demonstrated the discriminative capacity of the classification model.
T277 5971-6103 Sentence denotes The larger the AUROC, the better the performance of the model is for predicting the true positive (TP) and true negative (TN) cases.
T278 6104-6223 Sentence denotes The PRC shows the trade-off between the TPR and the positive predictive value (PPV) using different threshold settings.
T279 6224-6310 Sentence denotes The larger the AUPRC, the higher the capacity of the model is to predict the TP cases.
T280 6311-6425 Sentence denotes In our experiments, the CNNCF achieved high scores for both the AUPRC and AUROC (>99%) for the X-data and CT-data.
T281 6426-6552 Sentence denotes DL has made significant progress in numerous areas in recent years and has provided best-performance solutions for many tasks.
T282 6553-6736 Sentence denotes In areas that require high interpretability, such as autonomous driving and medical diagnosis, DL has disadvantages because it is a black-box approach and lacks good interpretability.
T283 6737-6924 Sentence denotes The strong correlation obtained between the CNNCF output and the experts’ evaluation suggested that the mechanism of the proposed CNNCF is similar to that used by humans analyzing images.
T284 6925-7089 Sentence denotes The combination of the visual interpretation and the correlation analysis enhanced the ability of the framework to interpret the results, making it highly reliable.
T285 7090-7216 Sentence denotes The CNNCF has a promising potential for clinical diagnosis considering its high performance and hybrid interpretation ability.
T286 7217-7649 Sentence denotes We have explored the potential use of the CNNCF for clinical diagnosis with the support of the Beijing Youan hospital (which is an authoritative hospital for the study of infectious diseases and one of the designated hospitals for COVID-19 treatment) using both real data after privacy masking and input from experts under experimental conditions and provided a suitable schedule for assisting experts with the radiography analysis.
T287 7650-7735 Sentence denotes However, medical diagnosis in a real situation is more complex than in an experiment.
T288 7736-7935 Sentence denotes Therefore, further studies will be conducted in different hospitals with different complexities and uncertainties to obtain more experience in multiple clinical use cases with the proposed framework.
T289 7936-8130 Sentence denotes The objective of this study was to use statistical methods to analyze the relationship between salient features in images and expert evaluations and test the discriminative ability of the model.
T290 8131-8326 Sentence denotes The CNNRF can be considered a cross-modal prediction model, which is a challenging research area that requires more attention because it is closely related to associative thinking and creativity.
T291 8327-8489 Sentence denotes In addition, the correlation analysis might be a possible optimization direction to improve the interpretability performance of the classification model using DL.
T292 8490-8714 Sentence denotes In conclusion, we proposed a complete framework for the computer-aided diagnosis of COVID-19, including data annotation, data preprocessing, model design, correlation analysis, and assessment of the model’s interpretability.
T293 8715-8854 Sentence denotes We developed a pseudo-color tool to convert the grayscale medical images to color images to facilitate image interpretation by the experts.
T294 8855-8981 Sentence denotes We developed a platform for the annotation of medical images characterized by high security, local sharing, and expandability.
T295 8982-9117 Sentence denotes We designed a simple data preprocessing method for converting multiple types of images (X-data, CT-data) to three-channel color images.
T296 9118-9285 Sentence denotes We established a modular CNN-based classification framework with high flexibility and wide use cases, consisting of the ResBlock-A, ResBlock-B, and Control Gate Block.
T297 9286-9445 Sentence denotes A knowledge distillation method was used as a training strategy for the proposed classification framework to ensure high performance with fast inference speed.
T298 9446-9693 Sentence denotes A CNN-based regression framework that required minimal changes to the architecture of the classification framework was employed to determine the correlation between the lesion area images of patients with COVID-19 and the five clinical indicators.
T299 9694-9913 Sentence denotes The three evaluation indices (F1, kappa, specificity) of the classification framework were similar to those of the respiratory resident and the emergency resident and slightly higher than that of the respiratory intern.
T300 9914-10043 Sentence denotes We visualized the salient features that contributed most to the CNNCF output in a heatmap for easy interpretability of the CNNCF.
T301 10044-10223 Sentence denotes The proposed CNNCF computer-aided diagnosis method showed relatively high precision and has a potential for the automatic diagnosis of COVID-19 in clinical practice in the future.
T302 10224-10333 Sentence denotes The outbreak of the COVID-19 epidemic poses serious threats to the safety and health of the human population.
T303 10334-10477 Sentence denotes At present, popular methods for the diagnosis and monitoring of viruses include the detection of viral RNAs using PCR or a test for antibodies.
T304 10478-10647 Sentence denotes However, one negative result of the RT-PCR test (especially in the areas of high infection risk) might not be enough to rule out the possibility of a COVID-19 infection.
T305 10648-10768 Sentence denotes On June 14, 2020, the Beijing Municipal Health Commission declared that strict management of fever clinics was required.
T306 10769-11035 Sentence denotes All medical institutions in Beijing were required to conduct tests to detect COVID-19 nucleic acids and antibodies, CT examinations, and the routine blood test (also referred to as “1 + 3 tests”) for patients with fever that live in areas with high infection risk51.
T307 11036-11237 Sentence denotes Therefore, the proposed computer-aided diagnosis using medical imaging could be used as an auxiliary diagnosis tool to help physicians identify people with high infection risk in the clinical workflow.
T308 11238-11313 Sentence denotes There is also a potential for broader applicability of the proposed method.
T309 11314-11467 Sentence denotes Once the method has been improved, it might be used in other diagnostic decision-making scenarios (lung cancer, liver cancer, etc.) using medical images.
T310 11468-11553 Sentence denotes The expertise of a specialist will be required in clinical cases in future scenarios.
T311 11554-11715 Sentence denotes However, we are optimistic about the potential of using DL methods in intelligent medicine and expect that many people will benefit from the advanced technology.