Approximating the Hotelling Observer with Autoencoder-Learned Efficient Channels for Binary Signal Detection Tasks
The objective assessment of image quality (IQ) has been advocated for the analysis and optimization of medical imaging systems. One method of obtaining such IQ metrics is through a mathematical observer. The Bayesian ideal observer is optimal by definition for signal detection tasks, but is frequent...
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Main Authors | , , |
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Format | Journal Article |
Language | English |
Published |
04.03.2020
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Subjects | |
Online Access | Get full text |
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Summary: | The objective assessment of image quality (IQ) has been advocated for the
analysis and optimization of medical imaging systems. One method of obtaining
such IQ metrics is through a mathematical observer. The Bayesian ideal observer
is optimal by definition for signal detection tasks, but is frequently both
intractable and non-linear. As an alternative, linear observers are sometimes
used for task-based image quality assessment. The optimal linear observer is
the Hotelling observer (HO). The computational cost of calculating the HO
increases with image size, making a reduction in the dimensionality of the data
desirable. Channelized methods have become popular for this purpose, and many
competing methods are available for computing efficient channels. In this work,
a novel method for learning channels using an autoencoder (AE) is presented.
AEs are a type of artificial neural network (ANN) that are frequently employed
to learn concise representations of data to reduce dimensionality. Modifying
the traditional AE loss function to focus on task-relevant information permits
the development of efficient AE-channels. These AE-channels were trained and
tested on a variety of signal shapes and backgrounds to evaluate their
performance. In the experiments, the AE-learned channels were competitive with
and frequently outperformed other state-of-the-art methods for approximating
the HO. The performance gains were greatest for the datasets with a small
number of training images and noisy estimates of the signal image. Overall, AEs
are demonstrated to be competitive with state-of-the-art methods for generating
efficient channels for the HO and can have superior performance on small
datasets. |
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DOI: | 10.48550/arxiv.2003.02321 |