# Ideal Observer Computation by Use of Markov-Chain Monte Carlo with Generative Adversarial Networks

arXiv: https://arxiv.org/abs/2304.00433 · HTML: https://mlsanity.com/paper/2304.00433/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 74 references that are in the map.
- First posted on arXiv 2 Apr 2023.

## Builds on (most cited first, 18 of 18)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Alias-Free Generative Adversarial Networks](https://mlsanity.com/paper/2106.12423/) (2021)
- [Rank-normalization, folding, and localization: An improved widehatR for assessing convergence of MCMC](https://mlsanity.com/paper/1903.08008/) (2019)
- [GANs for Medical Image Synthesis: An Empirical Study](https://mlsanity.com/paper/2105.05318/) (2021)
- [Assessing the Impact of Deep Neural Network-based Image Denoising on Binary Signal Detection Tasks](https://mlsanity.com/paper/2104.14037/) (2021)
- [Approximating the Ideal Observer and Hotelling Observer for binary signal detection tasks by use of supervised learning methods](https://mlsanity.com/paper/1905.06330/) (2019)
- [Learning stochastic object models from medical imaging measurements by use of advanced ambient generative adversarial networks](https://mlsanity.com/paper/2106.14324/) (2021)
- [Approximating the Ideal Observer for joint signal detection and localization tasks by use of supervised learning methods](https://mlsanity.com/paper/2006.00112/) (2020)
- [Assessing the ability of generative adversarial networks to learn canonical medical image statistics](https://mlsanity.com/paper/2204.12007/) (2022)
- [Markov-Chain Monte Carlo Approximation of the Ideal Observer using Generative Adversarial Networks](https://mlsanity.com/paper/2001.09526/) (2020)
- [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](https://mlsanity.com/paper/2110.12042/) (2021)
- [A signal detection model for quantifying over-regularization in non-linear image reconstruction](https://mlsanity.com/paper/2010.03472/) (2020)
- [Learning Numerical Observers using Unsupervised Domain Adaptation](https://mlsanity.com/paper/2002.03763/) (2020)
- [Advancing the AmbientGAN for learning stochastic object models](https://mlsanity.com/paper/2102.00281/) (2021)
- [Brain Tumor Synthetic Data Generation with Adaptive StyleGANs](https://mlsanity.com/paper/2212.01772/) (2022)

## Built on by (most cited first, 6 of 6)

- [AmbientFlow: Invertible generative models from incomplete, noisy measurements](https://mlsanity.com/paper/2309.04856/) (2023)
- [Ambient-Pix2PixGAN for Translating Medical Images from Noisy Data](https://mlsanity.com/paper/2402.01186/) (2024)
- [AmbientCycleGAN for Establishing Interpretable Stochastic Object Models Based on Mathematical Phantoms and Medical Imaging Measurements](https://mlsanity.com/paper/2402.01171/) (2024)
- [Ambient Denoising Diffusion Generative Adversarial Networks for Establishing Stochastic Object Models from Noisy Image Data](https://mlsanity.com/paper/2501.19094/) (2025)
- [Task-based Regularization in Penalized Least-Squares for Binary Signal Detection Tasks in Medical Image Denoising](https://mlsanity.com/paper/2501.18418/) (2025)
- [Using gradient of Lagrangian function to compute efficient channels for the ideal observer](https://mlsanity.com/paper/2501.19381/) (2025)

## Abstract (arXiv)

Medical imaging systems are often evaluated and optimized via objective, or task-specific, measures of image quality (IQ) that quantify the performance of an observer on a specific clinically-relevant task. The performance of the Bayesian Ideal Observer (IO) sets an upper limit among all observers, numerical or human, and has been advocated for use as a figure-of-merit (FOM) for evaluating and optimizing medical imaging systems. However, the IO test statistic corresponds to the likelihood ratio that is intractable to compute in the majority of cases. A sampling-based method that employs Markov-Chain Monte Carlo (MCMC) techniques was previously proposed to estimate the IO performance. However, current applications of MCMC methods for IO approximation have been limited to a small number of situations where the considered distribution of to-be-imaged objects can be described by a relatively simple stochastic object model (SOM). As such, there remains an important need to extend the domain of applicability of MCMC methods to address a large variety of scenarios where IO-based assessments are needed but the associated SOMs have not been available. In this study, a novel MCMC method that employs a generative adversarial network (GAN)-based SOM, referred to as MCMC-GAN, is described and evaluated. The MCMC-GAN method was quantitatively validated by use of test-cases for which reference solutions were available. The results demonstrate that the MCMC-GAN method can extend the domain of applicability of MCMC methods for conducting IO analyses of medical imaging systems.