DeepAMO: a multi-slice, multi-view anthropomorphic model observer for visual detection tasks performed on volume images
Abstract
Purpose: We propose a deep learning-based anthropomorphic model observer (DeepAMO) for image quality evaluation of multi-orientation, multi-slice image sets with respect to a clinically realistic 3D defect detection task. Approach: The DeepAMO is developed based on a hypothetical model of the decision process of a human reader performing a detection task using a 3D volume. The DeepAMO is comprised of three sequential stages: defect segmentation,...
Paper Details
Title
DeepAMO: a multi-slice, multi-view anthropomorphic model observer for visual detection tasks performed on volume images
Published Date
Jan 28, 2021
Journal
Volume
8
Issue
04
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