Górriz M, Giró-i-Nieto X, Carlier A, Faure E. Active Deep Learning for Medical Imaging Segmentation. In Medical Image meets NIPS 2017 Workshop. In Press.  (187.43 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.