Combining Image and Region Uncertainty-Based Active Learning for Melanoma Segmentation

Segmentation of medical images using learning-based systems remains a challenge in medical computer vision, as training a segmentation model requires exhaustively annotated medical images by experts, which are difficult and expensive to obtain. In the context of melanoma segmentation, we explored active learning methods to combine human annotation for the most uncertain pixels with model predictions for the others. With only around <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$30 \%$</tex> of the images annotated, we achieved performance similar to that obtained with a fully annotated dataset. We also demonstrated that, after a few iterations, experts can focus on annotating only the most uncertain areas of the images, relying on the model for the rest. These approaches pave the way for accelerating the annotation of unlabeled medical datasets and optimizing the use of medical expertise in deep learning projects.