76 citations · 100 across the 11 of their papers we have counts for
13 papers
Efficient and Scalable Physics-Guided Fully Convolutional Spatiotemporal Learning for 3D Microstructure Evolution Prediction
Michael Trimboli, Wenxi Liu, Xianqi Li
Accurate prediction of three-dimensional (3D) microstructure evolution remains computationally demanding because high-fidelity phase-field simulations require repeated numerical in…
Integrating Physics-Informed Neural Networks and 3D Vascular Geometry Learning for Cerebral Aneurysm Detection and Multimodal Rupture-Risk Prediction
Eshan Vipuil, Xianqi Li
Cerebral aneurysms are localized dilations of intracranial arteries that may rupture and cause subarachnoid hemorrhage. Current assessment relies on human interpretation of imaging…
AI-Augmented Adaptive Digital Twin Modeling for Brain Tumor Evolution Prediction and Treatment Scheduling
Wenxi Liu, Michael Trimboli, Xianqi Li
Brain tumor progression exhibits spatially heterogeneous growth, patient-specific treatment response, and complex interactions with surrounding anatomy, making accurate long-term p…
Physics-Guided Fully Convolutional Spatiotemporal Learning Toward Digital-Twin-Enabled Microstructure Evolution Prediction
Michael Trimboli, Wenxi Liu, Xianqi Li
Understanding and predicting microstructure evolution is central to materials design, yet purely data-driven spatiotemporal learning models often suffer from limited physical consi…
Fully Convolutional Spatiotemporal Learning for Microstructure Evolution Prediction
Michael Trimboli, Mohammed Alsubaie, Sirani M. Perera +2
Understanding and predicting microstructure evolution is fundamental to materials science, as it governs the resulting properties and performance of materials. Traditional simulati…
Conditional Denoising Diffusion Model-Based Robust MR Image Reconstruction from Highly Undersampled Data
Mohammed Alsubaie, Wenxi Liu, Linxia Gu +3
Magnetic Resonance Imaging (MRI) is a critical tool in modern medical diagnostics, yet its prolonged acquisition time remains a critical limitation, especially in time-sensitive cl…
Missing Data Estimation for MR Spectroscopic Imaging via Mask-Free Deep Learning Methods
Tan-Hanh Pham, Ovidiu C. Andronesi, Xianqi Li +1
Magnetic Resonance Spectroscopic Imaging (MRSI) is a powerful tool for non-invasive mapping of brain metabolites, providing critical insights into neurological conditions. However,…
A Low-complexity Structured Neural Network Approach to Intelligently Realize Wideband Multi-beam Beamformers
Hansaka Aluvihare, Sivakumar Sivasankar, Xianqi Li +2
True-time-delay (TTD) beamformers can produce wideband, squint-free beams in both analog and digital signal domains, unlike frequency-dependent FFT beams. Our previous work showed…
A Low-complexity Structured Neural Network to Realize States of Dynamical Systems
Hansaka Aluvihare, Levi Lingsch, Xianqi Li +1
Data-driven learning is rapidly evolving and places a new perspective on realizing state-space dynamical systems. However, dynamical systems derived from nonlinear ordinary differe…
SeUNet-Trans: A Simple yet Effective UNet-Transformer Model for Medical Image Segmentation
Tan-Hanh Pham, Xianqi Li, Kim-Doang Nguyen
Automated medical image segmentation is becoming increasingly crucial to modern clinical practice, driven by the growing demand for precise diagnosis, the push towards personalized…
Recent Advances in Generative AI for Healthcare Applications
Yasin Shokrollahi, Jose Colmenarez, Wenxi Liu +5
The rapid advancement of Artificial Intelligence (AI) has catalyzed revolutionary changes across various sectors, notably in healthcare. In particular, generative AI-led by diffusi…
Deep Learning-based Prediction of Stress and Strain Maps in Arterial Walls for Improved Cardiovascular Risk Assessment
Yasin Shokrollahi1, Pengfei Dong1, Xianqi Li +1
This study investigated the potential of end-to-end deep learning tools as a more effective substitute for FEM in predicting stress-strain fields within 2D cross sections of arteri…
Low Rank and Structured Modeling of High-dimensional Vector Autoregressions
Sumanta Basu, Xianqi Li, George Michailidis
Network modeling of high-dimensional time series data is a key learning task due to its widespread use in a number of application areas, including macroeconomics, finance and neuro…