Chunhao Wang, PhD

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...

Effective radiotherapy for upper abdominal tumors requires dose escalation but is limited by respiratory motion and the low gastrointestinal radiation tolerance. Current clinical motion management on conventional Linacs relies on simple external surrogates th...

To quantitatively benchmark dosimetric variation associated with tumor regression during head and neck (HN) radiotherapy and to evaluate the benefit of adaptive replanning as a basis for adapt-on-demand decision support.

To develop an in-silico tumor model that incorporates nutrient-driven growth and radiotherapy response to generate spatio-temporal proliferating (P), quiescent (Q), and necrotic (N) cell maps for radiomics-based heterogeneity analysis.

To develop a multi-parametric MRI (mp-MRI) radiomics framework for predicting post-resection glioblastoma (GBM) survival by integrating conventional MR modalities with a quantum mechanics–inspired imaging representation.

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...

Radiotherapy for upper abdominal cancers is limited by respiratory motion and the low radiation tolerance, restricting safe dose escalation. Conventional linear accelerators rely on kV X-ray and CBCT imaging but lack real-time internal motion tracking capabil...

To develop an in silico clinical trial framework based on mathematical modeling of tumor response to chemoradiation therapy as a prognostic indication of patient outcomes.