In recent years, medical image analysis has been boosted by ML and DL. It utilizes hierarchical recombination of features to extract relevant information and then learn the pattern representation by employing a multi-layer neural network. DL models have achieved massive progress in terms of algorithms, applications, and theories. ĭeep learning (DL) is a subset of ML, which aims at learning many levels of distributed representations of the data to be modeled. ML has been applied to different fields including robotics, pattern recognition, data mining, object recognition, face detection, and medical diagnosis. ML is a subfield of artificial intelligence (AI) which includes a wide range of computational algorithms and modeling tools utilized to process large numbers of data, where these algorithms aim to mimic human intelligence by learning from training data. The relatively recent development of very powerful computational hardware, such as graphic processioning units (GPUs), and the development of deep neural networks, paired with the availability of large quantities of digital data, have facilitated for machine learning (ML) to emerge as a field with the potential to generate great progress in different fields. The main contribution of this study is to provide an intensive review of the popular annotation tools and show their successful usage in annotating medical imaging dataset to guide researchers in this area. In this survey, we present the currently available annotation tools for medical imaging, including descriptions of graphical user interfaces (GUI) and supporting instruments. Different annotation tools have been developed to assist with the annotation process. Despite the huge success of deep learning algorithms in image analysis, training algorithms to reach human-level performance in these tasks depends on the availability of large amounts of high-quality training data, including high-quality annotations to serve as ground-truth. Deep learning and machine learning techniques provide different solutions for medical image interpretation including those associated with detection and diagnosis. It requires significant expertise to efficiently and correctly interpret the images generated by each of these technologies, which among others include radiography, ultrasound, and magnetic resonance imaging.

Medical imaging refers to several different technologies that are used to view the human body to diagnose, monitor, or treat medical conditions.

Annotation of an article examples