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Learning physics-based models from data: perspectives from inverse problems and model reduction
This article addresses the inference of physics models from data, from the perspectives of
inverse problems and model reduction. These fields develop formulations that integrate data …
inverse problems and model reduction. These fields develop formulations that integrate data …
Integrated biophysical modeling and image analysis: application to neuro-oncology
Central nervous system (CNS) tumors come with vastly heterogeneous histologic,
molecular, and radiographic landscapes, rendering their precise characterization …
molecular, and radiographic landscapes, rendering their precise characterization …
Predictive digital twin for optimizing patient-specific radiotherapy regimens under uncertainty in high-grade gliomas
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 …
aware clinical decision-making. We illustrate the methodology as an enabler for an …
TGM-Nets: A deep learning framework for enhanced forecasting of tumor growth by integrating imaging and modeling
Prediction and uncertainty quantification of tumor progression are vital in clinical practice, ie,
disease prognosis and decision-making on treatment strategies. In this work, we propose …
disease prognosis and decision-making on treatment strategies. In this work, we propose …
Modelling glioma progression, mass effect and intracranial pressure in patient anatomy
Increased intracranial pressure is the source of most critical symptoms in patients with
glioma, and often the main cause of death. Clinical interventions could benefit from non …
glioma, and often the main cause of death. Clinical interventions could benefit from non …
A fast and scalable computational framework for large-scale high-dimensional Bayesian optimal experimental design
We develop a fast and scalable computational framework to solve Bayesian optimal
experimental design problems governed by partial differential equations (PDEs) with …
experimental design problems governed by partial differential equations (PDEs) with …
A deep neural network for operator learning enhanced by attention and gating mechanisms for long-time forecasting of tumor growth
Forecasting tumor progression and assessing the uncertainty of predictions play a crucial
role in clinical settings, especially for determining disease outlook and making informed …
role in clinical settings, especially for determining disease outlook and making informed …
Quantitative in vivo imaging to enable tumour forecasting and treatment optimization
Current clinical decision-making in oncology relies on averages of large patient populations
to both assess tumour status and treatment outcomes. However, cancers exhibit an inherent …
to both assess tumour status and treatment outcomes. However, cancers exhibit an inherent …
Learn-Morph-Infer: a new way of solving the inverse problem for brain tumor modeling
Current treatment planning of patients diagnosed with a brain tumor, such as glioma, could
significantly benefit by accessing the spatial distribution of tumor cell concentration. Existing …
significantly benefit by accessing the spatial distribution of tumor cell concentration. Existing …
An offline-online decomposition method for efficient linear Bayesian goal-oriented optimal experimental design: Application to optimal sensor placement
Bayesian optimal experimental design (OED) plays an important role in minimizing model
uncertainty with limited experimental data in a Bayesian framework. In many applications …
uncertainty with limited experimental data in a Bayesian framework. In many applications …