arXiv subjects

Zhiqi Lee

Publications and source records attributed to Zhiqi Lee.

2 recordsLinked to original sources

arXiv2026

Multimodal risk trajectories reveal heterogeneous paths to dementia

Dementia comprises biologically heterogeneous disorders, yet current risk assessment provides limited insight into how subtype-specific risk emerges and diverges before clinical diagnosis. We developed NetMoint, a multimodal framework integrating partially observed plasma proteomic, structural magnetic resonance imaging and cerebral haemodynamic phenotypes to predict individualized risks of Alzheimer's disease (AD), vascular dementia (VD) and frontotemporal dementia (FTD) across 1-, 5-, 10- and 20-year horizons. Among 104,120 UK Biobank participants free of dementia at baseline, NetMoint achieved mean area under the receiver operating characteristic curve (AUC) values of 0.937, 0.930 and 0.932 for AD, VD and FTD, respectively. The biological determinants of prediction shifted with time, from structural brain vulnerability at shorter horizons towards circulating molecular signatures at longer horizons, with distinct subtype-specific biological profiles. Multi-horizon risk profiling identified distinct temporal trajectories of dementia susceptibility. Among participants who subsequently developed AD, 0.7% followed a persistently very-high-risk trajectory, with predicted risk reaching 53.50% at 20 years, whereas 8.3% of those who developed FTD followed an increasing very-high-risk trajectory, reaching 67.17%. These high-risk trajectories were marked by distinct molecular signatures, with lower TGFB1 characterizing the AD group and higher NDRG1 the FTD group. In an independent ADNI-to-UK Biobank analysis, AD risk prediction remained informative after harmonization to 138 shared features, with an AUC of 0.741 at 20 years. Together, these findings establish a multimodal framework for trajectory-resolved dementia risk stratification, identifying small but high-risk populations within dementia subtypes and linking their divergent risk trajectories to distinct molecular signatures.

q-bio.QM↗

arXiv2021

RA V-Net: Deep learning network for automated liver segmentation

Accurate segmentation of the liver is a prerequisite for the diagnosis of disease. Automated segmentation is an important application of computer-aided detection and diagnosis of liver disease. In recent years, automated processing of medical images has gained breakthroughs. However, the low contrast of abdominal scan CT images and the complexity of liver morphology make accurate automatic segmentation challenging. In this paper, we propose RA V-Net, which is an improved medical image automatic segmentation model based on U-Net. It has the following three main innovations. CofRes Module (Composite Original Feature Residual Module) is proposed. With more complex convolution layers and skip connections to make it obtain a higher level of image feature extraction capability and prevent gradient disappearance or explosion. AR Module (Attention Recovery Module) is proposed to reduce the computational effort of the model. In addition, the spatial features between the data pixels of the encoding and decoding modules are sensed by adjusting the channels and LSTM convolution. Finally, the image features are effectively retained. CA Module (Channel Attention Module) is introduced, which used to extract relevant channels with dependencies and strengthen them by matrix dot product, while weakening irrelevant channels without dependencies. The purpose of channel attention is achieved. The attention mechanism provided by LSTM convolution and CA Module are strong guarantees for the performance of the neural network. The accuracy of U-Net network: 0.9862, precision: 0.9118, DSC: 0.8547, JSC: 0.82. The evaluation metrics of RA V-Net, accuracy: 0.9968, precision: 0.9597, DSC: 0.9654, JSC: 0.9414. The most representative metric for the segmentation effect is DSC, which improves 0.1107 over U-Net, and JSC improves 0.1214.

eess.IV↗