Deep learning-driven glioblastoma diagnosis from histopathological images via single-cell segmentation and morphological analysis

Glioblastoma (GBM) exhibits a high recurrence rate of 95% due to its highly infiltrative nature and marked heterogeneity, making its diagnosis challenging. These characteristics complicate the standardization of diagnostic criteria and contribute to significant interpathologist variability. In respo...

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Xehetasun bibliografikoak

Egile Nagusiak:	Gonzalo Rosa-Olmeda, Sara Hiller-Vallina, Manuel Villa, Massimo Salvi, Ricardo Gargini, Miguel Chavarrías
Formatua:	Artigo
Hizkuntza:	Inglês
Argitaratua:	IOP Publishing 2025-01-01
Saila:	Machine Learning: Science and Technology
Gaiak:	glioblastoma pathological diagnosis hematoxylin and eosin single-cell analysis deep learning cell morphology
Sarrera elektronikoa:	https://doi.org/10.1088/2632-2153/ae1f5c
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