6 citations · 6 across the 6 of their papers we have counts for
6 papers
On the Limits of Maximal Coding Rate Reduction for Out-of-Distribution Generalisation
Menghui Zhou, Gaoshan Bi, Vitaveska Lanfranchi +1
Substantial efforts have been devoted to making deep learning objectives, representations, and architectures interpretable, with the goal of improving the safety, robustness, and g…
DeMMO: Longitudinal and Cross-Disease Modelling of Digital Mobility Outcomes via Multi-Task Learning
Menghui Zhou, Zhipeng Yuan, Vitaveska Lanfranchi +1
Digital mobility outcomes (DMOs) derived from wearable sensors characterise mobility in daily life and offer a promising means of monitoring disease progression. However, existing…
Collaborative Attention and Consistent-Guided Fusion of MRI and PET for Alzheimer's Disease Diagnosis
Delin Ma, Menghui Zhou, Jun Qi +2
Alzheimer's disease (AD) is the most prevalent form of dementia, and its early diagnosis is essential for slowing disease progression. Recent studies on multimodal neuroimaging fus…
Multi-Task Learning with Feature-Similarity Laplacian Graphs for Predicting Alzheimer's Disease Progression
Zixiang Xu, Menghui Zhou, Jun Qi +3
Alzheimer's Disease (AD) is the most prevalent neurodegenerative disorder in aging populations, posing a significant and escalating burden on global healthcare systems. While Multi…
Spatio-Temporal Similarity Measure based Multi-Task Learning for Predicting Alzheimer's Disease Progression using MRI Data
Xulong Wang, Yu Zhang, Menghui Zhou +3
Identifying and utilising various biomarkers for tracking Alzheimer's disease (AD) progression have received many recent attentions and enable helping clinicians make the prompt de…
An Attention-based Multi-Scale Feature Learning Network for Multimodal Medical Image Fusion
Meng Zhou, Xiaolan Xu, Yuxuan Zhang
Medical images play an important role in clinical applications. Multimodal medical images could provide rich information about patients for physicians to diagnose. The image fusion…