Lorenzo Venturini, Aris T. Papageorghiou, J. Alison Noble, Ana I. L. Namburete. Uncertainty Estimates as Data Selection Criteria to Boost Omni-Supervised Learning. In Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz, editors, Medical Image Computing and Computer Assisted Intervention - MICCAI 2020 - 23rd International Conference, Lima, Peru, October 4-8, 2020, Proceedings, Part I. Volume 12261 of Lecture Notes in Computer Science, pages 689-698, Springer, 2020. [doi]
@inproceedings{VenturiniPNN20,
title = {Uncertainty Estimates as Data Selection Criteria to Boost Omni-Supervised Learning},
author = {Lorenzo Venturini and Aris T. Papageorghiou and J. Alison Noble and Ana I. L. Namburete},
year = {2020},
doi = {10.1007/978-3-030-59710-8_67},
url = {https://doi.org/10.1007/978-3-030-59710-8_67},
researchr = {https://researchr.org/publication/VenturiniPNN20},
cites = {0},
citedby = {0},
pages = {689-698},
booktitle = {Medical Image Computing and Computer Assisted Intervention - MICCAI 2020 - 23rd International Conference, Lima, Peru, October 4-8, 2020, Proceedings, Part I},
editor = {Anne L. Martel and Purang Abolmaesumi and Danail Stoyanov and Diana Mateus and Maria A. Zuluaga and S. Kevin Zhou and Daniel Racoceanu and Leo Joskowicz},
volume = {12261},
series = {Lecture Notes in Computer Science},
publisher = {Springer},
isbn = {978-3-030-59710-8},
}