Deep reinforced active learning for multi-class image classification
- URL: http://arxiv.org/abs/2206.13391v1
- Date: Mon, 20 Jun 2022 09:30:55 GMT
- Title: Deep reinforced active learning for multi-class image classification
- Authors: Emma Slade, Kim M. Branson
- Abstract summary: High accuracy medical image classification can be limited by the costs of acquiring more data as well as the time and expertise needed to label existing images.
We apply active learning to medical image classification, a method which aims to maximise model performance on a minimal subset from a larger pool of data.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: High accuracy medical image classification can be limited by the costs of
acquiring more data as well as the time and expertise needed to label existing
images. In this paper, we apply active learning to medical image
classification, a method which aims to maximise model performance on a minimal
subset from a larger pool of data. We present a new active learning framework,
based on deep reinforcement learning, to learn an active learning query
strategy to label images based on predictions from a convolutional neural
network. Our framework modifies the deep-Q network formulation, allowing us to
pick data based additionally on geometric arguments in the latent space of the
classifier, allowing for high accuracy multi-class classification in a
batch-based active learning setting, enabling the agent to label datapoints
that are both diverse and about which it is most uncertain. We apply our
framework to two medical imaging datasets and compare with standard query
strategies as well as the most recent reinforcement learning based active
learning approach for image classification.
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