Region-based Active Learning for Reducing Annotation Effort of ECG Waveform Segmentation-Reference-Cited by-同舟云学术

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Region-based Active Learning for Reducing Annotation Effort of ECG Waveform Segmentation

Published:2021-10-29 Issue: Volume: Page:
ISSN:
Container-title:2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)
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Author:

Xiao Qiao¹,Yang Changyong²,Zhu Xiaoyan³,Wang Chaofeng⁴,Wan Yaping⁴,Liu Can⁵

Affiliation:

1. University of South China,School of Nursing,Hengyang,China

2. Hengyang No. 1 People’s Hospital,Emergency Department,Hengyang,China

3. University of South China,School of Nuclear Science and Technology,Hengyang,China

4. University of South China,School of Computer Science,Hengyang,China

5. Micron Technology Inc.,Department of R&D,Singapore City,Singapore

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9731095/9730887/09731214.pdf?arnumber=9731214

Reference18 articles.

1. Active Learning with Statistical Models

2. On uncertainty estimation in active learning for image segmentation;li;in Proceedings of 2020 International Conference on Machine Learning Workshop on Uncertainty and Robustness in Deep Learning,2020

3. Region-based active learning for efficient labeling in semantic segmentation

4. Active learning for accuracy enhancement of semantic segmentation with CNN-corrected label curations: Evaluation on kidney segmentation in abdominal CT

5. Active Learning Query Strategies for Classification, Regression, and Clustering: A Survey

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