Manojlović Teo, Tomanič Tadej, Štajduhar Ivan, Milanič Matija
University of Rijeka, Faculty of Engineering, Rijeka, Croatia.
University of Rijeka, Center for Artificial Intelligence and Cybersecurity, Rijeka, Croatia.
J Biomed Opt. 2025 Jan;30(1):016004. doi: 10.1117/1.JBO.30.1.016004. Epub 2025 Jan 16.
Machine learning models for the direct extraction of tissue parameters from hyperspectral images have been extensively researched recently, as they represent a faster alternative to the well-known iterative methods such as inverse Monte Carlo and inverse adding-doubling (IAD).
We aim to develop a Bayesian neural network model for robust prediction of physiological parameters from hyperspectral images.
We propose a two-component system for extracting physiological parameters from hyperspectral images. First, our system models the relationship between the measured spectra and the tissue parameters as a distribution rather than a point estimate and is thus able to generate multiple possible solutions. Second, the proposed tissue parameters are then refined using the neural network that approximates the biological tissue model.
The proposed model was tested on simulated and data. It outperformed current models with an overall mean absolute error of 0.0141 and can be used as a faster alternative to the IAD algorithm.
Results suggest that Bayesian neural networks coupled with the approximation of a biological tissue model can be used to reliably and accurately extract tissue properties from hyperspectral images on the fly.
近年来，用于从高光谱图像中直接提取组织参数的机器学习模型得到了广泛研究，因为它们是逆蒙特卡罗和逆加倍法（IAD）等知名迭代方法的更快替代方案。
我们旨在开发一种贝叶斯神经网络模型，用于从高光谱图像中稳健地预测生理参数。
我们提出了一种从高光谱图像中提取生理参数的双组件系统。首先，我们的系统将测量光谱与组织参数之间的关系建模为一种分布而非点估计，因此能够生成多个可能的解。其次，然后使用近似生物组织模型的神经网络对所提出的组织参数进行细化。
所提出的模型在模拟数据和真实数据上进行了测试。它以0.0141的总体平均绝对误差优于当前模型，并且可以用作IAD算法的更快替代方案。
结果表明，结合生物组织模型近似的贝叶斯神经网络可用于即时从高光谱图像中可靠且准确地提取组织特性。
