@inproceedings{f1bbf58482d24165a43800acf3404c21,
title = "Attention, suggestion and annotation: A deep active learning framework for biomedical image segmentation",
abstract = "Despite the great success, deep learning based segmentation methods still face a critical obstacle: the difficulty in acquiring sufficient training data due to high annotation costs. In this paper, we propose a deep active learning framework that combines the attention gated fully convolutional network (ag-FCN) and the distribution discrepancy based active learning algorithm (dd-AL) to significantly reduce the annotation effort by iteratively annotating the most informative samples to train the ag-FCN for the better segmentation performance. Our framework is evaluated on 2015 MICCAI Gland Segmentaion dataset and 2017 MICCAI 6-month infant brain MRI Segmentation dataset. Experiment results show that our framework can achieve state-of-the-art segmentation performance by using only a portion of the training data.",
author = "Haohan Li and Zhaozheng Yin",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020.; 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 ; Conference date: 04-10-2020 Through 08-10-2020",
year = "2020",
doi = "10.1007/978-3-030-59710-8\_1",
language = "English",
isbn = "9783030597092",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "3--13",
editor = "Martel, \{Anne L.\} and Purang Abolmaesumi and Danail Stoyanov and Diana Mateus and Zuluaga, \{Maria A.\} and Zhou, \{S. Kevin\} and Daniel Racoceanu and Leo Joskowicz",
booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings",
}