Górriz M, Giró-i-Nieto X, Carlier A, Faure E. Cost-Effective Active Learning for Melanoma Segmentation. In ML4H: Machine Learning for Health Workshop at NIPS 2017. Long Beach, CA, USA; In Press.  (519.78 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.