We propose a novel Active Learning framework capable to train effectively a convolutional neural network for semantic segmentation of medical imaging, with a limited amount of training labeled data. Our contribution is a practical Cost-Effective Active Learning approach using dropout at test time as Monte Carlo sampling to model the pixel-wise uncertainty and to analyze the image information to improve the training performance.
Górriz, M., Giro, X., Carlier, A., Faure, E. Cost-effective active learning for melanoma segmentation. A: Machine Learning for Health Workshop at NIPS. "ML4H: Machine Learning for Health NIPS, Workshop at NIPS 2017". Long Beach, CA: 2017, p. 1-5.