@inproceedings{6a1721edc52d4bd6b56f0245406dd0d7,
title = "Labeling cost sensitive batch active learning for brain tumor segmentation",
abstract = "Over the last decade, deep learning methods have achieved state-of-the-art for medical image segmentation tasks. However, the difficulty of obtaining sufficient labeled data can be a bottleneck. To this end, we design a novel active learning framework specially adapted to the brain tumor segmentation. Our approach includes a novel labeling cost designed to capture radiologists' practical labeling costs. This is combined with two acquisition functions to incorporate uncertainty and representation information, ensuring that the active learning selects informative and diverse data. The resulting procedure is a constrained combinatorial optimization problem. We propose an efficient algorithm for this task and demonstrate the proposed method's advantages for segmenting brain MRI data. ",
keywords = "Active Learning, Approximation Algorithm, Deep Learning, Segmentation, Uncertainty",
author = "Maohao Shen and Zhang, {Jacky Y.} and Leihao Chen and Weiman Yan and Neel Jani and Brad Sutton and Oluwasanmi Koyejo",
note = "Funding Information: This paper has been funded by the Jump ARCHES endowment through the Health Care Engineering Systems Center at University of Illinois at Urbana-Champaign. Oluwasanmi Koyejo acknowledges partial support from NSF 1934986. Publisher Copyright: {\textcopyright} 2021 IEEE.; 18th IEEE International Symposium on Biomedical Imaging, ISBI 2021 ; Conference date: 13-04-2021 Through 16-04-2021",
year = "2021",
month = apr,
day = "13",
doi = "10.1109/ISBI48211.2021.9434098",
language = "English (US)",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
pages = "1269--1273",
booktitle = "2021 IEEE 18th International Symposium on Biomedical Imaging, ISBI 2021",
}