RIL-Contour accelerates medical imaging annotation through the process of annotation by iterative deep learning (AID). To reduce annotation error, RIL-Contour promotes the standardization of image annotations across a dataset. RIL-Contour supports using fully automated deep-learning methods, semi-automated methods, and manual methods to annotate medical imaging with voxel and/or text annotations. A major goal driving the development of the software was to create an environment which enables clinically oriented users to utilize deep-learning models to rapidly annotate medical imaging. We developed RIL-Contour to accelerate medical image annotation for and with deep-learning. The effort to curate these datasets is widely regarded as a barrier to the development of deep-learning systems. Deep-learning models require large, diverse training datasets for optimal model convergence. Deep-learning algorithms typically fall within the domain of supervised artificial intelligence and are designed to “learn” from annotated data.
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