ArticleJournal of biomedical optics2024
Designing a use-error robust machine learning model for quantitative analysis of diffuse reflectance spectra.
Article in Journal of biomedical optics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed, 4 citations in OpenAlex.
- Exploring the role of sample thickness for hyperspectral microscopy tissue discrimination through Monte Carlo simulations.Biomedical optics express · 2025Article
- Robust estimation of skin physiological parameters from hyperspectral images using Bayesian neural networks.Journal of biomedical optics · 2025Article
- Learnable real-time inference of molecular composition from diffuse spectroscopy of brain tissue.Journal of biomedical optics · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Significance: Machine learning (ML)-enabled diffuse reflectance spectroscopy (DRS) is increasingly used as an alternative to the computation-intensive inverse Monte Carlo (MCI) simulation to predict tissue's optical properties, including the absorption coefficient, Aim: We aim to develop a use-error-robust ML algorithm for optical property prediction from DRS spectra. Approach: We developed a wavelength-independent regressor (WIR) to predict optical properties from DRS data. For validation, we generated 1520 simulated DRS spectra with the forward Monte Carlo model, where Results: When compounding all use-errors on simulated data, the WIR model best balanced accuracy and speed, yielding errors of 1.75% for Conclusions: The WIR model presents reliable use-error-robust optical property predictions from DRS data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.