most citedHOPE: Hybrid-granularity Ordinal Prototype Learning for Progression Prediction of Mild Cognitive Impairment

17 citations · 18 across the 2 of their papers we have counts for

collaborators

2 papers

eess.IV2024★ 1 cited

Low-dose CT Denoising with Language-engaged Dual-space Alignment

Zhihao Chen, Tao Chen, Chenhui Wang +3

While various deep learning methods were proposed for low-dose computed tomography (CT) denoising, they often suffer from over-smoothing, blurring, and lack of explainability. To a…

eess.IV2024★ 17 cited

HOPE: Hybrid-granularity Ordinal Prototype Learning for Progression Prediction of Mild Cognitive Impairment

Chenhui Wang, Yiming Lei, Tao Chen +3

Mild cognitive impairment (MCI) is often at high risk of progression to Alzheimer's disease (AD). Existing works to identify the progressive MCI (pMCI) typically require MCI subtyp…