A method of generating annotations corresponding to objects within images based on neural networks iteratively trained using the images. Active learning is used to train a machine learning model 256 by selecting data from unlabeled pool 254, annotating (e.g. by oracle 258) and adding them to training pool 252. Intelligent data point selection accelerates learning processes and reduces amount of data needed compared to training via randomly-acquired data sets. Increasing the frequency of uncertain data biases a training data set, wherein images causing higher uncertainty can be more relevant to include in the next round of active learning. Further training can also be based on: reusing images; Stein variational gradient descent SVGD; Dice log-likelihood; using mutual information between pools of annotated images and raw images, to select raw images for annotation with different profiles than training annotated images. Query‑by‑committee approach is used with active learning. Deep learning architectures comprise: Inception, ResNet-50, U-Net or V-Net. Used for medical image segmentation, e.g. volumetric imaging data, MRI and CT scans, where objects are human organs.
Image annotation using one or more neural networks