44 citations · 164 across the 12 of their papers we have counts for
12 papers
Enhancing medical vision-language contrastive learning via inter-matching relation modelling
Mingjian Li, Mingyuan Meng, Michael Fulham +3
Medical image representations can be learned through medical vision-language contrastive learning (mVLCL) where medical imaging reports are used as weak supervision through image-t…
Full-resolution MLPs Empower Medical Dense Prediction
Mingyuan Meng, Yuxin Xue, Dagan Feng +2
Dense prediction is a fundamental requirement for many medical vision tasks such as medical image restoration, registration, and segmentation. The most popular vision model, Convol…
PET Synthesis via Self-supervised Adaptive Residual Estimation Generative Adversarial Network
Yuxin Xue, Lei Bi, Yige Peng +3
Positron emission tomography (PET) is a widely used, highly sensitive molecular imaging in clinical diagnosis. There is interest in reducing the radiation exposure from PET but als…
AutoFuse: Automatic Fusion Networks for Deformable Medical Image Registration
Mingyuan Meng, Michael Fulham, Dagan Feng +2
Deformable image registration aims to find a dense non-linear spatial correspondence between a pair of images, which is a crucial step for many medical tasks such as tumor growth m…
Merging-Diverging Hybrid Transformer Networks for Survival Prediction in Head and Neck Cancer
Mingyuan Meng, Lei Bi, Michael Fulham +2
Survival prediction is crucial for cancer patients as it provides early prognostic information for treatment planning. Recently, deep survival models based on deep learning and med…
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…
A Dual-branch Self-supervised Representation Learning Framework for Tumour Segmentation in Whole Slide Images
Hao Wang, Euijoon Ahn, Jinman Kim
Supervised deep learning methods have achieved considerable success in medical image analysis, owing to the availability of large-scale and well-annotated datasets. However, creati…
Hyper-Connected Transformer Network for Multi-Modality PET-CT Segmentation
Lei Bi, Michael Fulham, Shaoli Song +2
[18F]-Fluorodeoxyglucose (FDG) positron emission tomography - computed tomography (PET-CT) has become the imaging modality of choice for diagnosing many cancers. Co-learning comple…
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…
Mixed reality hologram slicer (mxdR-HS): a marker-less tangible user interface for interactive holographic volume visualization
Hoijoon Jung, Younhyun Jung, Michael Fulham +1
Mixed reality head-mounted displays (mxdR-HMD) have the potential to visualize volumetric medical imaging data in holograms to provide a true sense of volumetric depth. An effectiv…