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Moving beyond simulation: data-driven quantitative photoacoustic imaging using tissue-mimicking phantoms
Accurate measurement of optical absorption coefficients from photoacoustic imaging (PAI)
data would enable direct map** of molecular concentrations, providing vital clinical …
data would enable direct map** of molecular concentrations, providing vital clinical …
Photoacoustic quantification of tissue oxygenation using conditional invertible neural networks
Intelligent systems in interventional healthcare depend on the reliable perception of the
environment. In this context, photoacoustic tomography (PAT) has emerged as a non …
environment. In this context, photoacoustic tomography (PAT) has emerged as a non …
Optimizing retinal prosthetic stimuli with conditional invertible neural networks
Implantable retinal prostheses offer a promising solution to restore partial vision by
circumventing damaged photoreceptor cells in the retina and directly stimulating the …
circumventing damaged photoreceptor cells in the retina and directly stimulating the …
Distribution-informed and wavelength-flexible data-driven photoacoustic oximetry
Significance Photoacoustic imaging (PAI) promises to measure spatially resolved blood
oxygen saturation but suffers from a lack of accurate and robust spectral unmixing methods …
oxygen saturation but suffers from a lack of accurate and robust spectral unmixing methods …
Enhancing synthetic training data for quantitative photoacoustic tomography with generative deep learning
Multiwavelength photoacoustic images encode information about a tissue's optical
absorption distribution. This can be used to estimate its blood oxygen saturation distribution …
absorption distribution. This can be used to estimate its blood oxygen saturation distribution …
[HTML][HTML] Efficient photoacoustic image synthesis with deep learning
Photoacoustic imaging potentially allows for the real-time visualization of functional human
tissue parameters such as oxygenation but is subject to a challenging underlying …
tissue parameters such as oxygenation but is subject to a challenging underlying …
Fusion of Multiple Data Sources for Vehicle Crashworthiness Prediction Using CycleGAN and Temporal Convolutional Networks
Computer-aided engineering (CAE) models play a pivotal role in predicting crashworthiness
of vehicle designs. While CAE models continue to advance in fidelity and accuracy, an …
of vehicle designs. While CAE models continue to advance in fidelity and accuracy, an …
Multispectral indices for real-time and non-invasive tissue ischemia monitoring using snapshot cameras
An adequate supply of oxygen-rich blood is vital to maintain cell homeostasis, cellular
metabolism, and overall tissue health. While classical methods of measuring tissue ischemia …
metabolism, and overall tissue health. While classical methods of measuring tissue ischemia …
LBC: Language-Based-Classifier for Out-Of-Variable Generalization
Large Language Models (LLMs) have great success in natural language processing tasks
such as response generation. However, their use in tabular data has been limited due to …
such as response generation. However, their use in tabular data has been limited due to …
A Novel Solution to the Inverse Kinematics Problem in Robotics Using Conditional Invertible Neural Networks
Solving of the inverse kinematics (IK) problem is a challenging topic in the field of robotic
arm controlling, due to the nonlinear, high computational costing, or ill-posed characteristic …
arm controlling, due to the nonlinear, high computational costing, or ill-posed characteristic …