44 citations · 164 across the 12 of their papers we have counts for
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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…