Medical imaging annotation is the main essence of machine learning development as far as the medical sector is concerned. Accurately annotated images help to train models with precision and a huge amount of such data is required for AI solutions. It helps to assess and predict with confidence.

Types of Annotation

In the field of medical imaging, annotation is implied to pay attention to areas of interest. The term annotation means to add metadata to the images to help a computer model spot the specific features. A medical image annotator typically executes one or two categories of annotation. In UK, the first type of medical annotation tool involves segmentation which means to classify single pixels. The process where you classify the whole image in a data set. In the second type of annotation, you can manipulate and encode the images in the DICOM (Digital Imaging and Communications in Medicine) format. There is yet another format called NIFTI that involves the production a 3D image

Skills Required for A Medical Annotator

The main requirement of a medical annotator lies in discerning what body parts may look like versus their appearance in the different pathologies. Some people may have a high level of affinity towards images or visuals when compared to others. If you have a great sense of visuals, you will have better knowledge of detail and precision. If you lack visual sense, your process of learning may go slow. Therefore, an annotator must have a great sense of the characteristics when it comes to rare cases of diseases.