activity
20182023
most citedMulti-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learning

13 citations · 23 across the 6 of their papers we have counts for

collaborators

8 papers

eess.IV2023

Accurate Airway Tree Segmentation in CT Scans via Anatomy-aware Multi-class Segmentation and Topology-guided Iterative Learning

Puyang Wang, Dazhou Guo, Dandan Zheng +8

Intrathoracic airway segmentation in computed tomography (CT) is a prerequisite for various respiratory disease analyses such as chronic obstructive pulmonary disease (COPD), asthm…

eess.IV20214 cited

Over-and-Under Complete Convolutional RNN for MRI Reconstruction

Pengfei Guo, Jeya Maria Jose Valanarasu, Puyang Wang +3

Reconstructing magnetic resonance (MR) images from undersampled data is a challenging problem due to various artifacts introduced by the under-sampling operation. Recent deep learn…

eess.IV202113 cited

Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learning

Pengfei Guo, Puyang Wang, Jinyuan Zhou +2

Fast and accurate reconstruction of magnetic resonance (MR) images from under-sampled data is important in many clinical applications. In recent years, deep learning-based methods…

eess.IV20202 cited

Confidence-guided Lesion Mask-based Simultaneous Synthesis of Anatomic and Molecular MR Images in Patients with Post-treatment Malignant Gliomas

Pengfei Guo, Puyang Wang, Rajeev Yasarla +3

Data-driven automatic approaches have demonstrated their great potential in resolving various clinical diagnostic dilemmas in neuro-oncology, especially with the help of standard a…

cs.CV20201 cited

Lesion Mask-based Simultaneous Synthesis of Anatomic and MolecularMR Images using a GAN

Pengfei Guo, Puyang Wang, Jinyuan Zhou +2

Data-driven automatic approaches have demonstrated their great potential in resolving various clinical diagnostic dilemmas for patients with malignant gliomas in neuro-oncology wit…

eess.IV20193 cited

Learning to Segment Brain Anatomy from 2D Ultrasound with Less Data

Jeya Maria Jose V., Rajeev Yasarla, Puyang Wang +2

Automatic segmentation of anatomical landmarks from ultrasound (US) plays an important role in the management of preterm neonates with a very low birth weight due to the increased…