. Cost-Effective Active Learning for Melanoma Segmentation. In ML4H: Machine Learning for Health Workshop at NIPS 2017. Long Beach, CA, USA; 2017. 
(521.82 KB)
(521.82 KB) Abstract
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.
