activity
20242026
most citedFull-resolution MLPs Empower Medical Dense Prediction

1 citations · 1 across the 1 of their papers we have counts for

collaborators

5 papers

cs.CV2026

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…

eess.IV20261 cited

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…

eess.IV2025

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…

eess.IV2024

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…

eess.IV2024

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…