238 citations · 378 across the 15 of their papers we have counts for
15 papers
Non-iterative Coarse-to-fine Transformer Networks for Joint Affine and Deformable Image Registration
Mingyuan Meng, Lei Bi, Michael Fulham +2
Image registration is a fundamental requirement for medical image analysis. Deep registration methods based on deep learning have been widely recognized for their capabilities to p…
CG-3DSRGAN: A classification guided 3D generative adversarial network for image quality recovery from low-dose PET images
Yuxin Xue, Yige Peng, Lei Bi +2
Positron emission tomography (PET) is the most sensitive molecular imaging modality routinely applied in our modern healthcare. High radioactivity caused by the injected tracer dos…
Brain Tumor Sequence Registration with Non-iterative Coarse-to-fine Networks and Dual Deep Supervision
Mingyuan Meng, Lei Bi, Dagan Feng +1
In this study, we focus on brain tumor sequence registration between pre-operative and follow-up Magnetic Resonance Imaging (MRI) scans of brain glioma patients, in the context of…
Radiomics-enhanced Deep Multi-task Learning for Outcome Prediction in Head and Neck Cancer
Mingyuan Meng, Lei Bi, Dagan Feng +1
Outcome prediction is crucial for head and neck cancer patients as it can provide prognostic information for early treatment planning. Radiomics methods have been widely used for o…
Automatic Tumor Segmentation via False Positive Reduction Network for Whole-Body Multi-Modal PET/CT Images
Yige Peng, Jinman Kim, Dagan Feng +1
Multi-modality Fluorodeoxyglucose (FDG) positron emission tomography / computed tomography (PET/CT) has been routinely used in the assessment of common cancers, such as lung cancer…
Non-iterative Coarse-to-fine Registration based on Single-pass Deep Cumulative Learning
Mingyuan Meng, Lei Bi, Dagan Feng +1
Deformable image registration is a crucial step in medical image analysis for finding a non-linear spatial transformation between a pair of fixed and moving images. Deep registrati…