@article{Eggert2021,
Author = {D. Eggert and M. Bengs and S. Westermann and N. Gessert and A. O. H. Gerstner and N. A. Mueller and J. Bewarder and A. Schlaefer and C. Betz,  and W. Laffers},
Title = {In vivo detection of head and neck tumors by hyperspectral imaging combined with deep learning methods.},
Journal = {<em>Journal of Biophotonics</em>.},
Year = {(2022).},
Volume = {<strong>15</strong>.},
Number = {(3),},
Pages = {e202100167},
Doi = {https://doi.org/10.1002/jbio.202100167},
Url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/jbio.202100167},
Keywords = {convolutional neural network, head and neck cancer, hyperspectral imaging, intraoperative imaging, optical biopsy},
Abstract = {Abstract Currently, there are no fast and accurate screening methods available for head and neck cancer, the eighth most common tumor entity. For this study, we used hyperspectral imaging, an imaging technique for quantitative and objective surface analysis, combined with deep learning methods for automated tissue classification. As part of a prospective clinical observational study, hyperspectral datasets of laryngeal, hypopharyngeal and oropharyngeal mucosa were recorded in 98 patients before surgery in vivo. We established an automated data interpretation pathway that can classify the tissue into healthy and tumorous using convolutional neural networks with 2D spatial or 3D spatio-spectral convolutions combined with a state-of-the-art Densenet architecture. Using 24 patients for testing, our 3D spatio-spectral Densenet classification method achieves an average accuracy of 81\%, a sensitivity of 83\% and a specificity of 79\%.}
}

@COMMENT{Bibtex file generated on 2026-5-13 with typo3 si_bibtex plugin. Data from https://www.tuhh.de/mtec/publications/2024-2020 }