LitCovid-sentences  

PMC:7782580 / 38880-42105 JSONTXT 3 Projects

Annnotations TAB TSV DIC JSON TextAE

Id Subject Object Predicate Lexical cue
T292 0-224 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 225-364 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 365-491 Sentence denotes We developed a platform for the annotation of medical images characterized by high security, local sharing, and expandability.
T295 492-627 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 628-795 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 796-955 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 956-1203 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 1204-1423 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 1424-1553 Sentence denotes We visualized the salient features that contributed most to the CNNCF output in a heatmap for easy interpretability of the CNNCF.
T301 1554-1733 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 1734-1843 Sentence denotes The outbreak of the COVID-19 epidemic poses serious threats to the safety and health of the human population.
T303 1844-1987 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 1988-2157 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 2158-2278 Sentence denotes On June 14, 2020, the Beijing Municipal Health Commission declared that strict management of fever clinics was required.
T306 2279-2545 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 2546-2747 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 2748-2823 Sentence denotes There is also a potential for broader applicability of the proposed method.
T309 2824-2977 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 2978-3063 Sentence denotes The expertise of a specialist will be required in clinical cases in future scenarios.
T311 3064-3225 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.