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

Authors

  • Renaud Bernatchez CERVO Brain Research Center Département de biochimie, de microbiologie et de bioinformatique, Université Laval
  • Audrey Durand Département d'informatique et de génie logiciel, Université Laval Département de génie électrique et de génie informatique, Université Laval Canada CIFAR AI Chair, Mila
  • Flavie Lavoie-Cardinal CERVO Brain Research Center Département de psychiatrie et de neurosciences, Université Laval

DOI:

https://doi.org/10.1609/aaai.v36i11.21593

Keywords:

Active Learning, Deep Learning, Machine Learning, Computer Vision, Biophotonics, Biomedical Imaging, Microscopy

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.
AAAI-22 Proceedings Cover

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Published

2022-06-28

How to Cite

Bernatchez, R., Durand, A., & Lavoie-Cardinal, F. (2022). Annotation Cost-Sensitive Deep Active Learning with Limited Data (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12913-12914. https://doi.org/10.1609/aaai.v36i11.21593

Issue

Vol. 36 No. 11: IAAI-22, EAAI-22, AAAI-22 Special Programs and Special Track, Student Papers and Demonstrations

Section

AAAI Student Abstract and Poster Program