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Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology
Digital twins employ mathematical and computational models to virtually represent a
physical object (eg, planes and human organs), predict the behavior of the object, and …
physical object (eg, planes and human organs), predict the behavior of the object, and …
Cardiac healthcare digital twins supported by artificial intelligence-based algorithms and extended reality—a systematic review
Recently, significant efforts have been made to create Health Digital Twins (HDTs), Digital
Twins for clinical applications. Heart modeling is one of the fastest-growing fields, which …
Twins for clinical applications. Heart modeling is one of the fastest-growing fields, which …
Novel approach to classify brain tumor based on transfer learning and deep learning
Transfer learning strategies were used to develop a unique method in the field of medicine.
Investigation in this study suggests an ensemble technique for early brain tumor detection …
Investigation in this study suggests an ensemble technique for early brain tumor detection …
Designing clinical trials for patients who are not average
The heterogeneity inherent in cancer means that even a successful clinical trial merely
results in a therapeutic regimen that achieves, on average, a positive result only in a subset …
results in a therapeutic regimen that achieves, on average, a positive result only in a subset …
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 …
Computer vision techniques for growth prediction: A prisma-based systematic literature review
Growth prediction technology is not only a practical application but also a crucial approach
that strengthens the safety of image processing techniques. By supplementing the growth …
that strengthens the safety of image processing techniques. By supplementing the growth …
SPBTGNS: Design of an Efficient Model for Survival Prediction in Brain Tumour Patients using Generative Adversarial Network with Neural Architectural Search …
The landscape of medical imaging, particularly in brain tumor analysis and survival
prediction, necessitates advancements due to the inherent complexities and life-threatening …
prediction, necessitates advancements due to the inherent complexities and life-threatening …
Ensemble inversion for brain tumor growth models with mass effect
We propose a method for extracting physics-based biomarkers from a single multiparametric
Magnetic Resonance Imaging (mpMRI) scan bearing a glioma tumor. We account for mass …
Magnetic Resonance Imaging (mpMRI) scan bearing a glioma tumor. We account for mass …
Med-Real2Sim: Non-Invasive Medical Digital Twins using Physics-Informed Self-Supervised Learning
A digital twin is a virtual replica of a real-world physical phenomena that uses mathematical
modeling to characterize and simulate its defining features. By constructing digital twins for …
modeling to characterize and simulate its defining features. By constructing digital twins for …
Bilo: Bilevel local operator learning for pde inverse problems
We propose a new neural network based method for solving inverse problems for partial
differential equations (PDEs) by formulating the PDE inverse problem as a bilevel …
differential equations (PDEs) by formulating the PDE inverse problem as a bilevel …