Share Email Print
cover

Proceedings Paper

Learning numerical observers using unsupervised domain adaptation
Author(s): Shenghua He; Weimin Zhou; Hua Li; Mark A. Anastasio
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Medical imaging systems are commonly assessed by use of objective image quality measures. Supervised deep learning methods have been investigated to implement numerical observers for task-based image quality assessment. However, labeling large amounts of experimental data to train deep neural networks is tedious, expensive, and prone to subjective errors. Computer-simulated image data can potentially be employed to circumvent these issues; however, it is often difficult to computationally model complicated anatomical structures, noise sources, and the response of real-world imaging systems. Hence, simulated image data will generally possess physical and statistical differences from the experimental image data they seek to emulate. Within the context of machine learning, these differences between the sets of two images is referred to as domain shift. In this study, we propose and investigate the use of an adversarial domain adaptation method to mitigate the deleterious effects of domain shift between simulated and experimental image data for deep learning-based numerical observers (DL-NOs) that are trained on simulated images but applied to experimental ones. In the proposed method, a DL-NO will initially be trained on computer-simulated image data and subsequently adapted for use with experimental image data, without the need for any labeled experimental images. As a proof of concept, a binary signal detection task is considered. The success of this strategy as a function of the degree of domain shift present between the simulated and experimental image data is investigated.

Paper Details

Date Published: 16 March 2020
PDF: 8 pages
Proc. SPIE 11316, Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment, 113160W (16 March 2020); doi: 10.1117/12.2549812
Show Author Affiliations
Shenghua He, Washington Univ. in St. Louis (United States)
Weimin Zhou, Washington Univ. in St. Louis (United States)
Hua Li, Carle Cancer Ctr., Carle Foundation Hospital (United States)
Univ. of Illinois at Urbana-Champaign (United States)
Mark A. Anastasio, Univ. of Illinois at Urbana-Champaign (United States)


Published in SPIE Proceedings Vol. 11316:
Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment
Frank W. Samuelson; Sian Taylor-Phillips, Editor(s)

© SPIE. Terms of Use
Back to Top
PREMIUM CONTENT
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?
close_icon_gray