Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
Assessing the ability of generative adversarial networks to learn canonical medical image statistics
In recent years, generative adversarial networks (GANs) have gained tremendous popularity
for potential applications in medical imaging, such as medical image synthesis, restoration …
for potential applications in medical imaging, such as medical image synthesis, restoration …
Synthetic data in radiological imaging: current state and future outlook
A key challenge for the development and deployment of artificial intelligence (AI) solutions in
radiology is solving the associated data limitations. Obtaining sufficient and representative …
radiology is solving the associated data limitations. Obtaining sufficient and representative …
Knowledge-based in silico models and dataset for the comparative evaluation of mammography AI for a range of breast characteristics, lesion conspicuities and doses
To generate evidence regarding the safety and efficacy of artificial intelligence (AI) enabled
medical devices, AI models need to be evaluated on a diverse population of patient cases …
medical devices, AI models need to be evaluated on a diverse population of patient cases …
The stochastic digital human is now enrolling for in silico imaging trials—methods and tools for generating digital cohorts
Randomized clinical trials, while often viewed as the highest evidentiary bar by which to
judge the quality of a medical intervention, are far from perfect. In silico imaging trials are …
judge the quality of a medical intervention, are far from perfect. In silico imaging trials are …
Report on the AAPM grand challenge on deep generative modeling for learning medical image statistics
Background The findings of the 2023 AAPM Grand Challenge on Deep Generative
Modeling for Learning Medical Image Statistics are reported in this Special Report. Purpose …
Modeling for Learning Medical Image Statistics are reported in this Special Report. Purpose …
Ambientflow: Invertible generative models from incomplete, noisy measurements
Generative models have gained popularity for their potential applications in imaging
science, such as image reconstruction, posterior sampling and data sharing. Flow-based …
science, such as image reconstruction, posterior sampling and data sharing. Flow-based …
Ideal observer computation by use of markov-chain monte carlo with generative adversarial networks
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 …
measures of image quality (IQ) that quantify the performance of an observer on a specific …
Application of learned ideal observers for estimating task-based performance bounds for computed imaging systems
Purpose The performance of the ideal observer (IO) acting on imaging measurements has
long been advocated as a figure-of-merit (FOM) to guide the optimization of imaging …
long been advocated as a figure-of-merit (FOM) to guide the optimization of imaging …
Ambient-Pix2PixGAN for translating medical images from noisy data
Image-to-image translation is a common task in computer vision and has been rapidly
increasing the impact on the field of medical imaging. Deep learning-based methods that …
increasing the impact on the field of medical imaging. Deep learning-based methods that …
Mining the manifolds of deep generative models for multiple data-consistent solutions of ill-posed tomographic imaging problems
Tomographic imaging is in general an ill-posed inverse problem. Typically, a single
regularized image estimate of the sought-after object is obtained from tomographic …
regularized image estimate of the sought-after object is obtained from tomographic …