KEYWORDS: Monte Carlo methods, Diffuse reflectance spectroscopy, In vivo imaging, Tissues, Optical imaging, Machine learning, Hyperspectral imaging, Functional imaging, Evolutionary algorithms, Data analysis
Simulations are indispensable in the field of biomedical optical imaging, particularly in functional imaging. Given the recent rise of artificial intelligence and the lack of labeled in vivo data, synthetic data is not only important for the validation of algorithms but also crucial for training machine learning methods. To support research based on synthetic data, we present a new framework for assessing the quality of synthetic spectral data. Experiments with more than 10,000 hyperspectral in vivo images obtained from multiple species and various organ classes indicate that our framework could become an important tool for researchers working with simulations.
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