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LitCovid-PubTator

Id Subject Object Predicate Lexical cue tao:has_database_id
123 56-64 Disease denotes COVID-19 MESH:C000657245
127 156-161 Species denotes human Tax:9606
128 184-189 Species denotes human Tax:9606
129 1477-1485 Disease denotes COVID-19 MESH:C000657245

LitCovid-sentences

Id Subject Object Predicate Lexical cue
T80 0-100 Sentence denotes A platform was developed for annotating lesion areas of COVID-19 in medical images (X-data, CT-data)
T81 101-264 Sentence denotes Medical imaging uses images of internal tissues of the human body or a part of the human body in a non-invasive manner for clinical diagnoses or treatment plans36.
T82 265-451 Sentence denotes Medical images (e.g., X-data and CT-data) are usually acquired using computed radiography and are typically stored in the Digital Imaging and Communications in Medicine (DICOM) format37.
T83 452-631 Sentence denotes X-data are two-dimensional grayscale images, and CT-data are three-dimensional data, consisting of slices of the data in the z axis direction of a two-dimensional grayscale image.
T84 632-747 Sentence denotes Machine learning methods are playing increasingly important roles in medical image analysis, especially DL methods.
T85 748-868 Sentence denotes DL uses multiple non-linear transformations to create a mapping relationship between the input data and output labels38.
T86 869-963 Sentence denotes The objective of this study was to annotate lesion areas in medical images with high accuracy.
T87 964-1152 Sentence denotes Therefore, we developed a pseudo-coloring method, which is a technique that helps enhance medical images for physicians to isolate relevant tissues and groups different tissues together39.
T88 1153-1313 Sentence denotes We converted the original grayscale images to color images using the open-source image processing tools Open Source Computer Vision Library (OpenCV) and Pillow.
T89 1314-1371 Sentence denotes Examples of the pseudo-color images are shown in Fig. 1a.
T90 1372-1511 Sentence denotes We developed a platform that uses a client-server architecture to annotate the potential lesion areas of COVID-19 on the CXR and CT images.
T91 1512-1591 Sentence denotes The platform can be deployed on a private cloud for security and local sharing.
T92 1592-1750 Sentence denotes All the images were annotated by two experienced radiologists (one was a 5th-year radiologist and the other was a 3rd-year radiologist) in the Youan Hospital.
T93 1751-1913 Sentence denotes If there was disagreement about a result, a senior radiologist and a respiratory doctor made the final decision to ensure the precision of the annotation process.
T94 1914-1987 Sentence denotes The details of the annotation pipeline are shown in Supplementary Fig. 1.
T95 1988-2094 Sentence denotes Fig. 1 Demonstrations of data preprocessing methods including pseudo-coloring and dimension normalization.
T96 2095-2160 Sentence denotes a Pseudo-coloring for abnormal examples in the CXR and CT images.
T97 2161-2293 Sentence denotes The original grayscale images were transformed into color images using the pseudo-coloring method and were annotated by the experts.
T98 2294-2437 Sentence denotes The scale bar on the right is the range of pixel values of the image data. b Dimension normalization to reduce the dimensions in the CT images.
T99 2438-2541 Sentence denotes The number of CT images were first resampled to a multiple of three and then divided into three groups.
T100 2542-2620 Sentence denotes Followed by the 1 × 1 convolution layers to reduce the dimensions of the data.