ArticleIEEE transactions on medical imaging2023
Assessing the Ability of Generative Adversarial Networks to Learn Canonical Medical Image Statistics.
Article in IEEE transactions on medical imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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Who cites it
14 citing papers in PubMed.
- An uncertainty-aware vision transformer-BiLSTM Bayesian framework for reliable clinical decision support using chest X-rays.Scientific reports · 2026Article
- FISM: harnessing deep learning and reinforcement learning for precision detection of microaneurysms and retinal exudates for early diabetic retinopathy diagnosis.BioData mining · 2025Article
- 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
- Parameter-optimized generative adversarial network framework for synthetic MRI generation: fine-tuning critical variables for enhanced image fidelity.Frontiers in medicine · 2025Article
- Generative Adversarial Networks With Radiomics Supervision for Lung Lesion Generation.IEEE transactions on bio-medical engineering · 2025Article
- A Novel Method for 3D Lung Tumor Reconstruction Using Generative Models.Diagnostics (Basel, Switzerland) · 2024Article
- Assessing the Capacity of a Denoising Diffusion Probabilistic Model to Reproduce Spatial Context.IEEE transactions on medical imaging · 2024Article
- Report on the AAPM Grand Challenge on deep generative modeling for learning medical image statistics.ArXiv · 2024Article
- Synthetic data in radiological imaging: current state and future outlook.BJR artificial intelligence · 2024Review
- G2NPAN: GAN-guided nuance perceptual attention network for multimodal medical fusion image quality assessment.Frontiers in neuroscience · 2024Article
- Ideal Observer Computation by Use of Markov-Chain Monte Carlo With Generative Adversarial Networks.IEEE transactions on medical imaging · 2023Article
- Enhancing the Super-Resolution of Medical Images: Introducing the Deep Residual Feature Distillation Channel Attention Network for Optimized Performance and Efficiency.Bioengineering (Basel, Switzerland) · 2023Article
- Contrast-Free Super-Resolution Power Doppler (CS-PD) Based on Deep Neural Networks.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2023Article
- Impact of GAN artifacts for simulating mammograms on identifying mammographically occult cancer.Journal of medical imaging (Bellingham, Wash.) · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
In recent years, generative adversarial networks (GANs) have gained tremendous popularity for potential applications in medical imaging, such as medical image synthesis, restoration, reconstruction, translation, as well as objective image quality assessment. Despite the impressive progress in generating high-resolution, perceptually realistic images, it is not clear if modern GANs reliably learn the statistics that are meaningful to a downstream medical imaging application. In this work, the ability of a state-of-the-art GAN to learn the statistics of canonical stochastic image models (SIMs) that are relevant to objective assessment of image quality is investigated. It is shown that although the employed GAN successfully learned several basic first- and second-order statistics of the specific medical SIMs under consideration and generated images with high perceptual quality, it failed to correctly learn several per-image statistics pertinent to the these SIMs, highlighting the urgent need to assess medical image GANs in terms of objective measures of image quality.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.