# Assessing the Impact of Deep Neural Network-based Image Denoising on Binary Signal Detection Tasks

arXiv: https://arxiv.org/abs/2104.14037 · HTML: https://mlsanity.com/paper/2104.14037/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2021.

## Built on by (most cited first, 22 of 22)

- [SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2108.06227/) (2021)
- [Learning stochastic object models from medical imaging measurements by use of advanced ambient generative adversarial networks](https://mlsanity.com/paper/2106.14324/) (2021)
- [Assessing the ability of generative adversarial networks to learn canonical medical image statistics](https://mlsanity.com/paper/2204.12007/) (2022)
- [Impact of deep learning-based image super-resolution on binary signal detection](https://mlsanity.com/paper/2107.02338/) (2021)
- [MEGAN: Memory Enhanced Graph Attention Network for Space-Time Video Super-Resolution](https://mlsanity.com/paper/2110.15327/) (2021)
- [Regression is all you need for medical image translation](https://mlsanity.com/paper/2505.02048/) (2025)
- [Ideal Observer Computation by Use of Markov-Chain Monte Carlo with Generative Adversarial Networks](https://mlsanity.com/paper/2304.00433/) (2023)
- [Adversarial Distortion Learning for Medical Image Denoising](https://mlsanity.com/paper/2204.14100/) (2022)
- [Investigating the limited performance of a deep-learning-based SPECT denoising approach: An observer-study-based characterization](https://mlsanity.com/paper/2203.01918/) (2022)
- [Need for Objective Task-based Evaluation of Deep Learning-Based Denoising Methods: A Study in the Context of Myocardial Perfusion SPECT](https://mlsanity.com/paper/2303.02110/) (2023)
- [Explainable AI for clinical risk prediction: a survey of concepts, methods, and modalities](https://mlsanity.com/paper/2308.08407/) (2023)
- [Enhancing signal detectability in learning-based CT reconstruction with a model observer inspired loss function](https://mlsanity.com/paper/2402.10010/) (2024)
- [DEMIST: A deep-learning-based task-specific denoising approach for myocardial perfusion SPECT](https://mlsanity.com/paper/2306.04249/) (2023)
- [Novel Hybrid Integrated Pix2Pix and WGAN Model with Gradient Penalty for Binary Images Denoising](https://mlsanity.com/paper/2407.11865/) (2024)
- [sFRC for assessing hallucinations in medical image restoration](https://mlsanity.com/paper/2603.04673/) (2026)
- [Survey of Deep Learning and Physics-Based Approaches in Computational Wave Imaging](https://mlsanity.com/paper/2410.08329/) (2024)
- [Estimating Task-based Performance Bounds for Accelerated MRI Image Reconstruction Methods by Use of Learned-Ideal Observers](https://mlsanity.com/paper/2501.09224/) (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)
- [A detection-task-specific deep-learning method to improve the quality of sparse-view myocardial perfusion SPECT images](https://mlsanity.com/paper/2504.16171/) (2025)
- [One-Step Is Optimal: Unconditional Rectified Flows are Noise2Noise Denoisers, and Multi-Step Integration Provably Hurts---A Benchmark and Task-Based Detectability Study on Low-Dose CT](https://mlsanity.com/paper/2609.31670/) (2026)
- [VascuSR: Iterative Anatomy-Aware Diffusion for CT Super-Resolution](https://mlsanity.com/paper/2610.20509/) (2026)

## Abstract (arXiv)

A variety of deep neural network (DNN)-based image denoising methods have been proposed for use with medical images. Traditional measures of image quality (IQ) have been employed to optimize and evaluate these methods. However, the objective evaluation of IQ for the DNN-based denoising methods remains largely lacking. In this work, we evaluate the performance of DNN-based denoising methods by use of task-based IQ measures. Specifically, binary signal detection tasks under signal-known-exactly (SKE) with background-known-statistically (BKS) conditions are considered. The performance of the ideal observer (IO) and common linear numerical observers are quantified and detection efficiencies are computed to assess the impact of the denoising operation on task performance. The numerical results indicate that, in the cases considered, the application of a denoising network can result in a loss of task-relevant information in the image. The impact of the depth of the denoising networks on task performance is also assessed. The presented results highlight the need for the objective evaluation of IQ for DNN-based denoising technologies and may suggest future avenues for improving their effectiveness in medical imaging applications.