Annotation Cost-Sensitive Deep Active Learning with Limited Data (Student Abstract)
Renaud Bernatchez, Audrey Durand, Flavie Lavoie-Cardinal
[AAAI-22] Student Abstract and Poster Program - FINALIST
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.
Introduction Video
Sessions where this paper appears
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Poster Session 6
Red 6
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Poster Session 11
Red 6
