PMC:7782580 / 30730-31953 JSONTXT

{"project":"LitCovid-PubTator","denotations":[{"id":"217","span":{"begin":209,"end":212},"obj":"Gene"}],"attributes":[{"id":"A217","pred":"tao:has_database_id","subj":"217","obj":"Gene:404663"}],"namespaces":[{"prefix":"Tax","uri":"https://www.ncbi.nlm.nih.gov/taxonomy/"},{"prefix":"MESH","uri":"https://id.nlm.nih.gov/mesh/"},{"prefix":"Gene","uri":"https://www.ncbi.nlm.nih.gov/gene/"},{"prefix":"CVCL","uri":"https://web.expasy.org/cellosaurus/CVCL_"}],"text":"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. 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. Our eyes are not highly sensitive to grayscale images in regions with high average brightness48, resulting in relatively low identification accuracy. The proposed pseudo-color method increased the information content of the medical images and facilitated the identification of the details. PCA has been widely used for feature extraction and dimensionality reduction in image processing49. We used PCA to determine the feature space of the sub-data sets. Each image in a specified sub-data set was represented as a linear combination of the eigenvectors. Since the eigenvectors describe the most informative regions in the medical images, they represent each sub-data set. We visualized the top-five eigenvectors of each sub-data set using an intuitive method."}

{"project":"LitCovid-PD-HP","denotations":[{"id":"T16","span":{"begin":480,"end":496},"obj":"Phenotype"}],"attributes":[{"id":"A16","pred":"hp_id","subj":"T16","obj":"http://purl.obolibrary.org/obo/HP_0041092"}],"text":"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. 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. Our eyes are not highly sensitive to grayscale images in regions with high average brightness48, resulting in relatively low identification accuracy. The proposed pseudo-color method increased the information content of the medical images and facilitated the identification of the details. PCA has been widely used for feature extraction and dimensionality reduction in image processing49. We used PCA to determine the feature space of the sub-data sets. Each image in a specified sub-data set was represented as a linear combination of the eigenvectors. Since the eigenvectors describe the most informative regions in the medical images, they represent each sub-data set. We visualized the top-five eigenvectors of each sub-data set using an intuitive method."}

{"project":"LitCovid-sentences","denotations":[{"id":"T237","span":{"begin":0,"end":284},"obj":"Sentence"},{"id":"T238","span":{"begin":285,"end":462},"obj":"Sentence"},{"id":"T239","span":{"begin":463,"end":612},"obj":"Sentence"},{"id":"T240","span":{"begin":613,"end":752},"obj":"Sentence"},{"id":"T241","span":{"begin":753,"end":852},"obj":"Sentence"},{"id":"T242","span":{"begin":853,"end":917},"obj":"Sentence"},{"id":"T243","span":{"begin":918,"end":1017},"obj":"Sentence"},{"id":"T244","span":{"begin":1018,"end":1135},"obj":"Sentence"},{"id":"T245","span":{"begin":1136,"end":1223},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"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. 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. Our eyes are not highly sensitive to grayscale images in regions with high average brightness48, resulting in relatively low identification accuracy. The proposed pseudo-color method increased the information content of the medical images and facilitated the identification of the details. PCA has been widely used for feature extraction and dimensionality reduction in image processing49. We used PCA to determine the feature space of the sub-data sets. Each image in a specified sub-data set was represented as a linear combination of the eigenvectors. Since the eigenvectors describe the most informative regions in the medical images, they represent each sub-data set. We visualized the top-five eigenvectors of each sub-data set using an intuitive method."}