ArticleIEEE transactions on medical imaging2022
A Hybrid Approach for Approximating the Ideal Observer for Joint Signal Detection and Estimation Tasks by Use of Supervised Learning and Markov-Chain Monte Carlo Methods.
Article in IEEE transactions on medical imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Approximating the ideal observer for joint signal detection and estimation tasks by the use of Markov-Chain Monte Carlo with generative adversarial networks.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Investigating the use of signal detection information in supervised learning-based image denoising with consideration of task-shift.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Application of learned ideal observers for estimating task-based performance bounds for computed imaging systems.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Ideal Observer Computation by Use of Markov-Chain Monte Carlo With Generative Adversarial Networks.IEEE transactions on medical imaging · 2023Article
- Nuclear Medicine and Artificial Intelligence: Best Practices for Evaluation (the RELAINCE Guidelines).Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The ideal observer (IO) sets an upper performance limit among all observers and has been advocated for assessing and optimizing imaging systems. For general joint detection and estimation (detection-estimation) tasks, estimation ROC (EROC) analysis has been established for evaluating the performance of observers. However, in general, it is difficult to accurately approximate the IO that maximizes the area under the EROC curve. In this study, a hybrid method that employs machine learning is proposed to accomplish this. Specifically, a hybrid approach is developed that combines a multi-task convolutional neural network and a Markov-Chain Monte Carlo (MCMC) method in order to approximate the IO for detection-estimation tasks. Unlike traditional MCMC methods, the hybrid method is not limited to use of specific utility functions. In addition, a purely supervised learning-based sub-ideal observer is proposed. Computer-simulation studies are conducted to validate the proposed method, which include signal-known-statistically/background-known-exactly and signal-known-statistically/background-known-statistically tasks. The EROC curves produced by the proposed method are compared to those produced by the MCMC approach or analytical computation when feasible. The proposed method provides a new approach for approximating the IO and may advance the application of EROC analysis for optimizing imaging systems.
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Registered trials
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