| 000 | 03002nab a2200457 c 4500 | ||
|---|---|---|---|
| 001 | koha000995957 | ||
| 005 | 20230306164614.0 | ||
| 007 | cr | | ||
| 008 | 230130|2022 ru s a eng d | ||
| 024 | 7 |
_a10.18287/JBPE22.08.010304 _2doi |
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| 035 | _akoha000995957 | ||
| 040 |
_aRU-ToGU _brus _cRU-ToGU |
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| 245 | 1 | 0 |
_aInvasive and minimally invasive optical detection of pigment accumulation in brain cortex _cL. R. Oliveira, T. M. Gonçalves, M. R. Pinheiro [et al.] |
| 336 | _aТекст | ||
| 337 | _aэлектронный | ||
| 504 | _aБиблиогр.: 50 назв. | ||
| 520 | 3 | _aThe estimation of the spectral absorption coefficient of biological tissues provides valuable information that can be used in diagnostic procedures. Such estimation can be made using direct calculations from invasive spectral measurements or though machine learning algorithms based on noninvasive or minimally invasive spectral measurements. Since in a noninvasive approach, the number of measurements is limited, an exploratory study to investigate the use of artificial generated data in machine learning techniques was performed to evaluate the spectral absorption coefficient of the brain cortex. Considering the spectral absorption coefficient that was calculated directly from invasive measurements as reference, the similar spectra that were estimated through different machine learning approaches were able to provide comparable information in terms of pigment, DNA and blood contents in the cortex. The best estimated results were obtained based only on the experimental measurements, but it was also observed that artificially generated spectra can be used in the estimations to increase accuracy, provided that a significant number of experimental spectra are available both to generate the complementary artificial spectra and to estimate the resulting absorption spectrum of the tissue. | |
| 653 | _aдиффузное отражение | ||
| 653 | _aспектроскопия ткани | ||
| 653 | _aкоэффициент поглощения | ||
| 653 | _aмашинное обучение | ||
| 653 | _aгенеративные модели | ||
| 653 | _aкора головного мозга | ||
| 653 | _aкровь | ||
| 653 | _aДНК | ||
| 655 | 4 |
_aстатьи в журналах _9875125 |
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| 700 | 1 |
_aOliveira, Luís R. _9875124 |
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| 700 | 1 |
_aGonçalves, Tânia M. _9875126 |
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| 700 | 1 |
_aPinheiro, Maria R. _9875127 |
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| 700 | 1 |
_aFernandes, Luís E. _9875128 |
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| 700 | 1 |
_aMartins, Inês Soraia _9855295 |
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| 700 | 1 |
_aSilva, Hugo Filipe _9855297 |
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| 700 | 1 |
_aOliveira, Hélder P. _9816988 |
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| 700 | 1 |
_aTuchin, Valery V. _978712 |
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| 700 | 1 |
_aOliveira, Luís Manuel _9855298 |
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| 773 | 0 |
_tJournal of biomedical photonics & engineering _d2022 _gVol. 8, № 1. P. 010304-1-010304-16 _x2411-2844 |
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| 852 | 4 | _aRU-ToGU | |
| 856 | 4 | _uhttp://vital.lib.tsu.ru/vital/access/manager/Repository/koha:000995957 | |
| 908 | _aстатья | ||
| 999 | _c995957 | ||