238 citations · 743 across the 26 of their papers we have counts for
26 papers
Hyper-Fusion Network for Semi-Automatic Segmentation of Skin Lesions
Lei Bi, Michael Fulham, Jinman Kim
Automatic skin lesion segmentation methods based on fully convolutional networks (FCNs) are regarded as the state-of-the-art for accuracy. When there are, however, insufficient tra…
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…
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…