Annotation

About: Annotation is a research topic. Over the lifetime, 6719 publications have been published within this topic receiving 203463 citations. The topic is also known as: note & markup.

Annotation security to prevent the divulgence of sensitive information

Abstract: Methods, systems, and articles of manufacture that may be used to prevent sensitive information from being divulged in an annotation are provided. Upon creation, modification and/or retrieval of an annotation, a set of predefined security rules may be applied to the annotation, in an effort to detect sensitive information contained therein. Upon detecting sensitive information in an annotation, appropriate security measures may be taken, such as notifying a user creating, modifying, or retrieving the annotation (e.g., prompting the user to modify annotation to remove the sensitive information), preventing entry of the annotation, and/or notifying appropriate personnel in charge of security, such as a system administrator.

System and method for creating and displaying image annotations

Abstract: Disclosed herein are computer-implemented systems and methods for allowing an end-user to create and share annotated comments, modify published images, and/or otherwise interact with images published on digital content platforms (e.g., images published on a webpage, mobile application, etc.). The systems and methods may include: (1) providing an annotation interface to allow a first end-user to create an annotation on a published image; (2) providing a comment entry interface to receive a comment from the first end-user; (3) linking the annotation and the comment; (4) identifying when a second end-user accesses the image or comment on the digital content platform; and (5) displaying the comment and/or annotation to the second end-user.

Genome and proteome annotation: organization, interpretation and integration

Abstract: Recent years have seen a huge increase in the generation of genomic and proteomic data. This has been due to improvements in current biological methodologies, the development of new experimental techniques and the use of computers as support tools. All these raw data are useless if they cannot be properly analysed, annotated, stored and displayed. Consequently, a vast number of resources have been created to present the data to the wider community. Annotation tools and databases provide the means to disseminate these data and to comprehend their biological importance. This review examines the various aspects of annotation: type, methodology and availability. Moreover, it puts a special interest on novel annotation fields, such as that of phenotypes, and highlights the recent efforts focused on the integrating annotations.

VinDr-CXR: An open dataset of chest X-rays with radiologist's annotations

Abstract: Most of the existing chest X-ray datasets include labels from a list of findings without specifying their locations on the radiographs. This limits the development of machine learning algorithms for the detection and localization of chest abnormalities. In this work, we describe a dataset of more than 100,000 chest X-ray scans that were retrospectively collected from two major hospitals in Vietnam. Out of this raw data, we release 18,000 images that were manually annotated by a total of 17 experienced radiologists with 22 local labels of rectangles surrounding abnormalities and 6 global labels of suspected diseases. The released dataset is divided into a training set of 15,000 and a test set of 3,000. Each scan in the training set was independently labeled by 3 radiologists, while each scan in the test set was labeled by the consensus of 5 radiologists. We designed and built a labeling platform for DICOM images to facilitate these annotation procedures. All images are made publicly available in DICOM format in company with the labels of the training set. The labels of the test set are hidden at the time of writing this paper as they will be used for benchmarking machine learning algorithms on an open platform.