Integrating spatial and chemical information enhances differentiation of non-alcoholic steatohepatitis states in Raman imaging

Abstract Machine learning studies for Raman imaging have addressed the differentiability of normal and diseased states in biomedical applications by grouping a set of Raman spectra in terms of spectral similarity over the sample. However, Raman imaging provides both chemical information relevant to...

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Autors principals:	Ryoya Kondo, Yuta Mizuno, Kentaro Mochizuki, Kosuke Hashimoto, Jean-Emmanuel Clément, Yasuaki Kumamoto, Katsumasa Fujita, Yoshinori Harada, Tamiki Komatsuzaki
Format:	Artigo
Idioma:	Inglês
Publicat:	Nature Portfolio 2025-10-01
Col·lecció:	Scientific Reports
Matèries:	Raman microscopy Information Bottleneck Method Machine learning Clustering Segmentation Hyperspectral images
Accés en línia:	https://doi.org/10.1038/s41598-025-17495-z
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