23 citations · 26 across the 7 of their papers we have counts for
8 papers
sFRC for assessing hallucinations in medical image restoration
Prabhat Kc, Rongping Zeng, Nirmal Soni +1
Deep learning (DL) methods are currently being explored to restore images from sparse-view-, limited-data-, and undersampled-based acquisitions in medical applications. Although ou…
Evaluating the resolution of AI-based accelerated MR reconstruction using a deep learning-based model observer
Zitong Yu, Rongping Zeng, Frank Samuelson +1
Deep Learning-based Model Observers (DLMOs) were developed to evaluate a multi-coil sensitivity encoding parallel MRI at different acceleration factors on the Rayleigh discriminati…
Hallucinations in medical devices
Jason Granstedt, Prabhat Kc, Rucha Deshpande +2
Computer methods in medical devices are frequently imperfect and are known to produce errors in clinical or diagnostic tasks. However, when deep learning and data-based approaches…
Estimating Task-based Performance Bounds for Accelerated MRI Image Reconstruction Methods by Use of Learned-Ideal Observers
Kaiyan Li, Prabhat Kc, Hua Li +3
Medical imaging systems are commonly assessed and optimized by the use of objective measures of image quality (IQ). The performance of the ideal observer (IO) acting on imaging mea…
Assessing the performance of CT image denoisers using Laguerre-Gauss Channelized Hotelling Observer for lesion detection
Prabhat Kc, Rongping Zeng
The remarkable success of deep learning methods in solving computer vision problems, such as image classification, object detection, scene understanding, image segmentation, etc.,…
Report on the AAPM Grand Challenge on deep generative modeling for learning medical image statistics
Rucha Deshpande, Varun A. Kelkar, Dimitrios Gotsis +5
The findings of the 2023 AAPM Grand Challenge on Deep Generative Modeling for Learning Medical Image Statistics are reported in this Special Report. The goal of this challenge was…
Evaluating Procedures for Establishing Generative Adversarial Network-based Stochastic Image Models in Medical Imaging
Varun A. Kelkar, Dimitrios S. Gotsis, Frank J. Brooks +4
Modern generative models, such as generative adversarial networks (GANs), hold tremendous promise for several areas of medical imaging, such as unconditional medical image synthesi…
Deep neural networks-based denoising models for CT imaging and their efficacy
Prabhat KC, Rongping Zeng, M. Mehdi Farhangi +1
Most of the Deep Neural Networks (DNNs) based CT image denoising literature shows that DNNs outperform traditional iterative methods in terms of metrics such as the RMSE, the PSNR…