@conference {cGorriz, title = {Active Deep Learning for Medical Imaging Segmentation}, booktitle = {Medical Image meets NIPS 2017 Workshop}, year = {2017}, month = {11/2017}, 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.

Active Deep Learning for Medical Imaging from Xavier Giro-i-Nieto
}, author = {G{\'o}rriz, Marc and Xavier Gir{\'o}-i-Nieto and Carlier, Axel and Faure, Emmanuel} }