ArticleIEEE transactions on medical imaging2021
Assessing the Impact of Deep Neural Network-Based Image Denoising on Binary Signal Detection Tasks.
Article in IEEE transactions on medical imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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34 citing papers in PubMed.
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- Survey of Deep Learning and Physics-Based Approaches in Computational Wave Imaging.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2025Article
- On the utility of virtual staining for downstream applications as it relates to task network capacity.Biomedical optics express · 2025Article
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- Characterization of artificial intelligence performance for lesion detection using synthetic lesions in PET imaging.Medical physics · 2025Article
- A practical approach to the spatial-domain calculation of nonprewhitening model observers in computed tomography.Medical physics · 2025Article
- Article
- A detection-task-specific deep-learning method to improve the quality of sparse-view myocardial perfusion SPECT images.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- ISIT-GEN: An in silico imaging trial to assess the inter-scanner generalizability of CTLESS for myocardial perfusion SPECT on defect-detection task.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- CTLESS: A scatter-window projection and deep learning-based transmission-less attenuation compensation method for myocardial perfusion SPECT.IEEE transactions on medical imaging · 2024Article
- 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
- DEMIST: A Deep-Learning-Based Detection-Task-Specific Denoising Approach for Myocardial Perfusion SPECT.IEEE transactions on radiation and plasma medical sciences · 2024Article
- Need for Objective Task-Based Evaluation of Image Segmentation Algorithms for Quantitative PET: A Study with ACRIN 6668/RTOG 0235 Multicenter Clinical Trial Data.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2024Article
- Application of learned ideal observers for estimating task-based performance bounds for computed imaging systems.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Learned Full Waveform Inversion Incorporating Task Information for Ultrasound Computed Tomography.IEEE transactions on computational imaging · 2024Article
- Ideal Observer Computation by Use of Markov-Chain Monte Carlo With Generative Adversarial Networks.IEEE transactions on medical imaging · 2023Article
- Deep learning denoising of digital breast tomosynthesis: Observer performance study of the effect on detection of microcalcifications in breast phantom images.Medical physics · 2023Article
- Task-based assessment of digital mammography microcalcification detection with deep learning denoising algorithmss usingJournal of medical imaging (Bellingham, Wash.) · 2023Article
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4 authors.
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Abstract
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.
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