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• W3013080024 endingPage "1911" @default.
• W3013080024 startingPage "1911" @default.
• W3013080024 abstract "Hyperspectral imaging (HSI) technology has demonstrated potential to provide useful information about the chemical composition of tissue and its morphological features in a single image modality. Deep learning (DL) techniques have demonstrated the ability of automatic feature extraction from data for a successful classification. In this study, we exploit HSI and DL for the automatic differentiation of glioblastoma (GB) and non-tumor tissue on hematoxylin and eosin (H&E) stained histological slides of human brain tissue. GB detection is a challenging application, showing high heterogeneity in the cellular morphology across different patients. We employed an HSI microscope, with a spectral range from 400 to 1000 nm, to collect 517 HS cubes from 13 GB patients using 20× magnification. Using a convolutional neural network (CNN), we were able to automatically detect GB within the pathological slides, achieving average sensitivity and specificity values of 88% and 77%, respectively, representing an improvement of 7% and 8% respectively, as compared to the results obtained using RGB (red, green, and blue) images. This study demonstrates that the combination of hyperspectral microscopic imaging and deep learning is a promising tool for future computational pathologies." @default.
• W3013080024 created "2020-04-03" @default.
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• W3013080024 date "2020-03-30" @default.
• W3013080024 modified "2023-10-16" @default.
• W3013080024 title "Hyperspectral Imaging for the Detection of Glioblastoma Tumor Cells in H&E Slides Using Convolutional Neural Networks" @default.
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• W3013080024 doi "https://doi.org/10.3390/s20071911" @default.
• W3013080024 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7181269" @default.
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