Annotation Cost-Sensitive Deep Active Learning with Limited Data (Student Abstract)

Renaud Bernatchez, Audrey Durand, Flavie Lavoie-Cardinal

AAAI 2022 pp. 12913-12914

doi:10.1609/AAAI.V36I11.21593 /aaai/2022/bernatchez2022aaai-annotation/

Abstract

Deep learning is a promising avenue to automate tedious analysis tasks in biomedical imaging. However, its application in such a context is limited by the large amount of labeled data required to train deep learning models. While active learning may be used to reduce the amount of labeling data, many approaches do not consider the cost of annotating, which is often significant in a biomedical imaging setting. In this work we show how annotation cost can be considered and learned during active learning on a classification task on the MNIST dataset.

PDF AAAI Semantic Scholar

Cite

Text

Bernatchez et al. "Annotation Cost-Sensitive Deep Active Learning with Limited Data (Student Abstract)." AAAI Conference on Artificial Intelligence, 2022. doi:10.1609/AAAI.V36I11.21593

Markdown

[Bernatchez et al. "Annotation Cost-Sensitive Deep Active Learning with Limited Data (Student Abstract)." AAAI Conference on Artificial Intelligence, 2022.](https://mlanthology.org/aaai/2022/bernatchez2022aaai-annotation/) doi:10.1609/AAAI.V36I11.21593

BibTeX

@inproceedings{bernatchez2022aaai-annotation,
  title     = {{Annotation Cost-Sensitive Deep Active Learning with Limited Data (Student Abstract)}},
  author    = {Bernatchez, Renaud and Durand, Audrey and Lavoie-Cardinal, Flavie},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2022},
  pages     = {12913-12914},
  doi       = {10.1609/AAAI.V36I11.21593},
  url       = {https://mlanthology.org/aaai/2022/bernatchez2022aaai-annotation/}
}