Prof. Sašo Džeroski Profile

Prof. Sašo Džeroski

at Jožef Stefan Institut

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Publications (2)

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Proceedings Article | 22 July 2019 Paper

Hybrid technique for characterization of human skin using a combined machine learning and inverse Monte Carlo approach

N. Verdel, J. Tanevski, S. Džeroski, B. Majaron

Proceedings Volume 11075, 110751K (2019) https://doi.org/10.1117/12.2526997

KEYWORDS: Skin, Data modeling, Blood, Scattering, Monte Carlo methods, Machine learning, Tissue optics, Diffuse reflectance spectroscopy

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We have recently introduced a novel methodology for noninvasive assessment of structure and composition of human skin in vivo. The approach combines pulsed photothermal radiometry (PPTR), involving time-resolved measurements of midinfrared emission after irradiation with a millisecond light pulse, and diffuse reflectance spectroscopy (DRS) in visible part of the spectrum (400–600 nm). The experimental data are fitted simultaneously with respective predictions from a four-layer Monte Carlo (MC) model of light transport in human skin. The described approach allows assessment of the contents of specific chromophores (melanin, oxy-, and deoxyhemoglobin), as well as scattering properties and thicknesses of the epidermis and dermis. However, the involved multidimensional optimization with a numerical forward model (i.e., inverse MC, IMC) is computationally very expensive. In addition, each optimization task is repeated several times to control the inevitable numerical noise and facilitate escape from local minima. Thus, assessment of 14 free parameters from each radiometric transient and DRS spectrum takes several hours despite massive parallelization using CUDA technology and a high-performance graphics card. To alleviate this limitation, we have developed a computationally very efficient predictive model (PM) based on machine learning technology. The PM is an ensemble of decision trees (random forest), trained using ~10,000 "pairs" of various skin parameter combinations and the corresponding PPTR signals and DRS spectra, computed using our forward MC model. While the parameter values predicted by the PM are very similar to the IMC results there are some concerns regarding their accuracy. Therefore, we present here a hybrid model, which combines the described PM and IMC approaches.

Proceedings Article | 26 February 2019 Presentation + Paper

A machine-learning model for quantitative characterization of human skin using photothermal radiometry and diffuse reflectance spectroscopy

N. Verdel, J. Tanevski, S. Džeroski, B. Majaron

Proceedings Volume 10851, 1085107 (2019) https://doi.org/10.1117/12.2509691

KEYWORDS: Skin, Data modeling, Scattering, Blood, Resolution enhancement technologies, Diffuse reflectance spectroscopy, Radiometry, Monte Carlo methods, Tissue optics, Performance modeling

Read Abstract +

We have recently introduced a novel methodology for noninvasive assessment of structure and composition of human skin in vivo. The approach combines pulsed photothermal radiometry (PPTR), involving time-resolved measurements of mid-infrared emission after irradiation with a millisecond light pulse, and diffuse reflectance spectroscopy (DRS) in visible part of the spectrum (400–600 nm). The experimental data are fitted simultaneously with respective predictions from a four-layer Monte Carlo (MC) model of light transport in human skin. The described approach allows assessment of the contents of specific chromophores (melanin, oxy-, and deoxyhemoglobin), as well as scattering properties and thicknesses of the epidermis and dermis. However, the involved multidimensional optimization with a numerical forward model (i.e., inverse MC) is computationally very expensive. In addition, each optimization task is repeated several times to control the inevitable numerical noise and facilitate escape from local minima. Thus, assessment of 14 free parameters from each radiometric transient and DRS spectrum takes several hours despite massive parallelization using CUDA technology and a high-performance graphics card. To alleviate this limitation, we have constructed a computationally very efficient predictive model (PM) based on machine learning. The PM is an ensemble of decision trees (random forest), trained using ~11,000 "pairs" of various skin parameter combinations and the corresponding PPTR signals and DRS spectra, computed using our forward MC model. We analyze the performance of such a PM by means of cross-validation and comparison with the inverse MC approach.

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