86 citations · 190 across the 9 of their papers we have counts for
17 papers
X-ray Dissectography Improves Lung Nodule Detection
Chuang Niu, Giridhar Dasegowda, Pingkun Yan +2
Although radiographs are the most frequently used worldwide due to their cost-effectiveness and widespread accessibility, the structural superposition along the x-ray paths often r…
AI-Enabled Ultra-Low-Dose CT Reconstruction
Weiwen Wu, Chuang Niu, Shadi Ebrahimian +3
By the ALARA (As Low As Reasonably Achievable) principle, ultra-low-dose CT reconstruction is a holy grail to minimize cancer risks and genetic damages, especially for children. Wi…
Deep Interactive Denoiser (DID) for X-Ray Computed Tomography
Ti Bai, Biling Wang, Dan Nguyen +5
Low dose computed tomography (LDCT) is desirable for both diagnostic imaging and image guided interventions. Denoisers are openly used to improve the quality of LDCT. Deep learning…
Deep Metric Learning-based Image Retrieval System for Chest Radiograph and its Clinical Applications in COVID-19
Aoxiao Zhong, Xiang Li, Dufan Wu +17
In recent years, deep learning-based image analysis methods have been widely applied in computer-aided detection, diagnosis and prognosis, and has shown its value during the public…
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…
Quantifying and Leveraging Predictive Uncertainty for Medical Image Assessment
Florin C. Ghesu, Bogdan Georgescu, Awais Mansoor +11
The interpretation of medical images is a challenging task, often complicated by the presence of artifacts, occlusions, limited contrast and more. Most notable is the case of chest…