Active Learning

Active learning as a framework has a straight objective to select the data points that would be most beneficial for the deep learning model to learn from. As a cyclic framework it consists of Training pool, a machine learning, a strategy to estimate uncertainty of unlabeled data points, unlabeled pool of data and an Oracle that annotates the data. The cyclic framework is in short a data engine that is designed to keep improving the machine learning model.

In MONAI label, the developer to create their own scoring strategy based on which the next sample can be chosen. All sample-apps are equipped with a basic skeleton to allow for the development of scoring strategy. For example here in the DeepGrow Spleen app.