English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The ideal observer (IO) sets an upper performance limit among all observersand has been advocated for assessing and optimizing imaging systems. Forgeneral joint detection and estimation (detection-estimation) tasks, estimationROC (EROC) analysis has been established for evaluating the performance ofobservers. However, in general, it is difficult to accurately approximate theIO that maximizes the area under the EROC curve. In this study, a hybrid methodthat employs machine learning is proposed to accomplish this. Specifically, ahybrid approach is developed that combines a multi-task convolutional neuralnetwork and a Markov-Chain Monte Carlo (MCMC) method in order to approximatethe IO for detection-estimation tasks. Unlike traditional MCMC methods, thehybrid 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 andsignal-known-statistically/background-known-statistically tasks. The EROCcurves produced by the proposed method are compared to those produced by theMCMC approach or analytical computation when feasible. The proposed methodprovides a new approach for approximating the IO and may advance theapplication of EROC analysis for optimizing imaging systems.

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