100 citations · 132 across the 6 of their papers we have counts for
6 papers
Validating the predictions of mathematical models describing tumor growth and treatment response
Guillermo Lorenzo, David A. Hormuth, Chengyue Wu +7
Despite advances in methods to interrogate tumor biology, the observational and population-based approach of classical cancer research and clinical oncology does not enable anticip…
Predictive Digital Twin for Optimizing Patient-Specific Radiotherapy Regimens under Uncertainty in High-Grade Gliomas
Anirban Chaudhuri, Graham Pash, David A. Hormuth +6
We develop a methodology to create data-driven predictive digital twins for optimal risk-aware clinical decision-making. We illustrate the methodology as an enabler for an anticipa…
Hierarchical Diffusion Autoencoders and Disentangled Image Manipulation
Zeyu Lu, Chengyue Wu, Xinyuan Chen +4
Diffusion models have attained impressive visual quality for image synthesis. However, how to interpret and manipulate the latent space of diffusion models has not been extensively…
Generative Data Augmentation for Non-IID Problem in Decentralized Clinical Machine Learning
Zirui Wang, Shaoming Duan, Chengyue Wu +4
Swarm learning (SL) is an emerging promising decentralized machine learning paradigm and has achieved high performance in clinical applications. SL solves the problem of a central…
An untrained deep learning method for reconstructing dynamic magnetic resonance images from accelerated model-based data
Kalina P. Slavkova, Julie C. DiCarlo, Viraj Wadhwa +5
The purpose of this work is to implement physics-based regularization as a stopping condition in tuning an untrained deep neural network for reconstructing MR images from accelerat…
A 1D-0D-3D coupled model for simulating blood flow and transport processes in breast tissue
Marvin Fritz, Tobias Köppl, J. Tinsley Oden +3
In this work, we present mixed dimensional models for simulating blood flow and transport processes in breast tissue and the vascular tree supplying it. These processes are conside…