69 citations · 149 across the 8 of their papers we have counts for
8 papers
DULDA: Dual-domain Unsupervised Learned Descent Algorithm for PET image reconstruction
Rui Hu, Yunmei Chen, Kyungsang Kim +3
Deep learning based PET image reconstruction methods have achieved promising results recently. However, most of these methods follow a supervised learning paradigm, which rely heav…
A Noise-level-aware Framework for PET Image Denoising
Ye Li, Jianan Cui, Junyu Chen +7
In PET, the amount of relative (signal-dependent) noise present in different body regions can be significantly different and is inherently related to the number of counts present i…
Deep Learning-based Four-region Lung Segmentation in Chest Radiography for COVID-19 Diagnosis
Young-Gon Kim, Kyungsang Kim, Dufan Wu +10
Purpose. Imaging plays an important role in assessing severity of COVID 19 pneumonia. However, semantic interpretation of chest radiography (CXR) findings does not include quantita…
Consensus Neural Network for Medical Imaging Denoising with Only Noisy Training Samples
Dufan Wu, Kuang Gong, Kyungsang Kim +1
Deep neural networks have been proved efficient for medical image denoising. Current training methods require both noisy and clean images. However, clean images cannot be acquired…
Computationally Efficient Deep Neural Network for Computed Tomography Image Reconstruction
Dufan Wu, Kyungsang Kim, Quanzheng Li
Deep-neural-network-based image reconstruction has demonstrated promising performance in medical imaging for under-sampled and low-dose scenarios. However, it requires large amount…
Learning Personalized Representation for Inverse Problems in Medical Imaging Using Deep Neural Network
Kuang Gong, Kyungsang Kim, Jianan Cui +4
Recently deep neural networks have been widely and successfully applied in computer vision tasks and attracted growing interests in medical imaging. One barrier for the application…
End-to-end Lung Nodule Detection in Computed Tomography
Dufan Wu, Kyungsang Kim, Bin Dong +2
Computer aided diagnostic (CAD) system is crucial for modern med-ical imaging. But almost all CAD systems operate on reconstructed images, which were optimized for radiologists. Co…
A Cascaded Convolutional Neural Network for X-ray Low-dose CT Image Denoising
Dufan Wu, Kyungsang Kim, Georges El Fakhri +1
Image denoising techniques are essential to reducing noise levels and enhancing diagnosis reliability in low-dose computed tomography (CT). Machine learning based denoising methods…