10 citations · 11 across the 6 of their papers we have counts for
7 papers
MedBOE: Optimization-Based Estimation of Complete Patient Body Models from Whole-Body Medical Imaging
Hoijoon Jung, Yuhao Wei, Mingxiao Tu +3
True whole-body medical imaging captures a patient from head to toe and provides comprehensive anatomical and/or functional information. This coverage enables reconstruction of the…
Heterogeneity-Aware Deep Learning for Tumour Classification from Multiparametric MRI
Yue Xia, Euijoon Ahn, Tian Xia +3
Intra-tumoural heterogeneity (ITH) reflects spatial variation in tumour biology and is an important determinant of tumour behaviour, prognosis, and treatment response. Radiomics an…
MAISY: Motion-Aware Image SYnthesis for Medical Image Motion Correction
Andrew Zhang, Hao Wang, Shuchang Ye +2
Patient motion during medical image acquisition causes blurring, ghosting, and distorts organs, which makes image interpretation challenging. Current state-of-the-art algorithms us…
Advancing Deformable Medical Image Registration with Multi-axis Cross-covariance Attention
Mingyuan Meng, Michael Fulham, Lei Bi +1
Deformable image registration is a fundamental requirement for medical image analysis. Recently, transformers have been widely used in deep learning-based registration methods for…
Language-guided Medical Image Segmentation with Target-informed Multi-level Contrastive Alignments
Mingjian Li, Mingyuan Meng, Shuchang Ye +4
Medical image segmentation is a fundamental task in numerous medical engineering applications. Recently, language-guided segmentation has shown promise in medical scenarios where 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…
AdaMSS: Adaptive Multi-Modality Segmentation-to-Survival Learning for Survival Outcome Prediction from PET/CT Images
Mingyuan Meng, Bingxin Gu, Michael Fulham +4
Survival prediction is a major concern for cancer management. Deep survival models based on deep learning have been widely adopted to perform end-to-end survival prediction from me…