Assessing the ability of generative adversarial networks to learn canonical medical image statistics
| Autor: | Varun A. Kelkar, Dimitrios S. Gotsis, Frank J. Brooks, KC Prabhat, Kyle J. Myers, Rongping Zeng, Mark A. Anastasio |
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| Rok vydání: | 2023 |
| Předmět: |
FOS: Computer and information sciences
Radiological and Ultrasound Technology Computer Vision and Pattern Recognition (cs.CV) Image and Video Processing (eess.IV) ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Computer Science - Computer Vision and Pattern Recognition FOS: Physical sciences Electrical Engineering and Systems Science - Image and Video Processing Physics - Medical Physics Computer Science Applications FOS: Electrical engineering electronic engineering information engineering Medical Physics (physics.med-ph) Electrical and Electronic Engineering Software |
| Zdroj: | IEEE Transactions on Medical Imaging. :1-1 |
| ISSN: | 1558-254X 0278-0062 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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