Author profile
Duke Kunshan University
To develop a pixel-level uncertainty-aware consistency (UA-Cons) learning framework to optimize the feature compensation behavior of deep neural networks in scenarios where multi-parametric MRI modalities are incomplete.
To propose a federated learning (FL) framework incorporating a novel deep ensemble strategy for multi-institutional brain metastasis (BM) segmentation, improving performance in limited local datasets while preserving privacy by avoiding large-scale data trans...
To develop and evaluate a federated learning (FL) framework for brain metastasis (BM) segmentation that integrates an uncertainty score into a novel FL objective, improving segmentation robustness and potentially performance when training on limited-size data...
To evaluate whether a federated learning (FL) scheme that leverages adult glioma patient data improves multi-parametric MRI (mp-MRI) based pediatric glioma segmentation.
Automated segmentation of lung nodules in chest CT is critical for early cancer screening but remains challenging due to the small size and variable morphology of nodules, which often resemble vessels or pleura. This study proposes a novel framework integrati...
Our previous work proposed a Neural ODE–based U-Net (NODE-UNet) that generates continuous trajectories to visualize the evolution of feature representations from the initial input to the terminal state. We hypothesize that modeling contextual consistency alon...
To enhance breast malignancy prediction, this study develops a multimodal framework that integrates automated, Vision-Language Model (VLM)-derived BI-RADS lexicons with quantitative radiomic features.