📝How to improve medical datasets: 4 small tips
Getting the right data in the right amount - before ordering datasets of medical images, project managers need to coordinate with teams of machine learning, data science and clinical researchers. This will help overcome the difficulty of getting “bad” data or having annotation teams filter through thousands of irrelevant or low quality images and videos when annotating training data, which is costly and time consuming.
Empowering annotator teams with AI-based tools - annotating medical images for machine learning models requires precision, efficiency, high levels of quality, and security. With AI-based image annotation tools, medical annotators and specialists can save hours of work and generate more accurately labeled medical images.
Ensuring ease of data transfer - clinical data should be delivered and communicated in a format that is easy to parse, annotate, port, and after annotation, quickly and efficiently transfer to an ML model.
Overcome the complexities of storage and transmission - medical image data often consists of hundreds or thousands of terabytes that cannot simply be mailed. Project managers need to ensure the end-to-end security and efficiency of purchasing or retrieving, cleaning, storing and transferring medical data