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Optimal joint detection and estimation that maximizes ROC-type curves

Adam Wunderlich, Bart Goossens and Craig K. Abbey (2016) IEEE TRANSACTIONS ON MEDICAL IMAGING. 35(9). p.2164-2173
abstract
Combined detection-estimation tasks are frequently encountered in medical imaging. Optimal methods for joint detection and estimation are of interest because they provide upper bounds on observer performance, and can potentially be utilized for imaging system optimization, evaluation of observer efficiency, and development of image formation algorithms. We present a unified Bayesian framework for decision rules that maximize receiver operating characteristic (ROC)-type summary curves, including ROC, localization ROC (LROC), estimation ROC (EROC), free-response ROC (FROC), alternative free-response ROC (AFROC), and exponentially-transformed FROC (EFROC) curves, succinctly summarizing previous results. The approach relies on an interpretation of ROC-type summary curves as plots of an expected utility versus an expected disutility (or penalty) for signal-present decisions. We propose a general utility structure that is flexible enough to encompass many ROC variants and yet sufficiently constrained to allow derivation of a linear expected utility equation that is similar to that for simple binary detection. We illustrate our theory with an example comparing decision strategies for joint detection-estimation of a known signal with unknown amplitude. In addition, building on insights from our utility framework, we propose new ROC-type summary curves and associated optimal decision rules for joint detection-estimation tasks with an unknown, potentially-multiple, number of signals in each observation.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
TRAUMATIC BRAIN-INJURY, OF-THE-ART, DETECTION-LOCALIZATION, OBSERVER-PERFORMANCE, IDEAL OBSERVERS, OPTIMIZATION, BIOMARKERS, STENOSIS, SIGNALS, LESIONS, Ideal observer, receiver operating characteristic, signal detection, theory, expected utility theory
journal title
IEEE TRANSACTIONS ON MEDICAL IMAGING
IEEE Trans. Med. Imaging
volume
35
issue
9
pages
10 pages
publisher
Ieee-inst Electrical Electronics Engineers Inc
place of publication
Piscataway
Web of Science type
Article
Web of Science id
000385266800016
JCR category
COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
JCR impact factor
3.942 (2016)
JCR rank
9/105 (2016)
JCR quartile
1 (2016)
ISSN
0278-0062
DOI
10.1109/TMI.2016.2553001
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
8521889
handle
http://hdl.handle.net/1854/LU-8521889
date created
2017-05-31 17:23:38
date last changed
2017-09-15 14:03:49
@article{8521889,
  abstract     = {Combined detection-estimation tasks are frequently encountered in medical imaging. Optimal methods for joint detection and estimation are of interest because they provide upper bounds on observer performance, and can potentially be utilized for imaging system optimization, evaluation of observer efficiency, and development of image formation algorithms. We present a unified Bayesian framework for decision rules that maximize receiver operating characteristic (ROC)-type summary curves, including ROC, localization ROC (LROC), estimation ROC (EROC), free-response ROC (FROC), alternative free-response ROC (AFROC), and exponentially-transformed FROC (EFROC) curves, succinctly summarizing previous results. The approach relies on an interpretation of ROC-type summary curves as plots of an expected utility versus an expected disutility (or penalty) for signal-present decisions. We propose a general utility structure that is flexible enough to encompass many ROC variants and yet sufficiently constrained to allow derivation of a linear expected utility equation that is similar to that for simple binary detection. We illustrate our theory with an example comparing decision strategies for joint detection-estimation of a known signal with unknown amplitude. In addition, building on insights from our utility framework, we propose new ROC-type summary curves and associated optimal decision rules for joint detection-estimation tasks with an unknown, potentially-multiple, number of signals in each observation.},
  author       = {Wunderlich, Adam and Goossens, Bart and Abbey, Craig K.},
  issn         = {0278-0062},
  journal      = {IEEE TRANSACTIONS ON MEDICAL IMAGING},
  keyword      = {TRAUMATIC BRAIN-INJURY,OF-THE-ART,DETECTION-LOCALIZATION,OBSERVER-PERFORMANCE,IDEAL OBSERVERS,OPTIMIZATION,BIOMARKERS,STENOSIS,SIGNALS,LESIONS,Ideal observer,receiver operating characteristic,signal detection,theory,expected utility theory},
  language     = {eng},
  number       = {9},
  pages        = {2164--2173},
  publisher    = {Ieee-inst Electrical Electronics Engineers Inc},
  title        = {Optimal joint detection and estimation that maximizes ROC-type curves},
  url          = {http://dx.doi.org/10.1109/TMI.2016.2553001},
  volume       = {35},
  year         = {2016},
}

Chicago
Wunderlich, Adam, Bart Goossens, and Craig K. Abbey. 2016. “Optimal Joint Detection and Estimation That Maximizes ROC-type Curves.” Ieee Transactions on Medical Imaging 35 (9): 2164–2173.
APA
Wunderlich, A., Goossens, B., & Abbey, C. K. (2016). Optimal joint detection and estimation that maximizes ROC-type curves. IEEE TRANSACTIONS ON MEDICAL IMAGING, 35(9), 2164–2173.
Vancouver
1.
Wunderlich A, Goossens B, Abbey CK. Optimal joint detection and estimation that maximizes ROC-type curves. IEEE TRANSACTIONS ON MEDICAL IMAGING. Piscataway: Ieee-inst Electrical Electronics Engineers Inc; 2016;35(9):2164–73.
MLA
Wunderlich, Adam, Bart Goossens, and Craig K. Abbey. “Optimal Joint Detection and Estimation That Maximizes ROC-type Curves.” IEEE TRANSACTIONS ON MEDICAL IMAGING 35.9 (2016): 2164–2173. Print.