Machine Learning Streamlines the Morphometric Characterization and Multiclass Segmentation of Nuclei in Different Follicular Thyroid Lesions: Everything in a NUTSHELL.

MLA

L’Imperio, Vincenzo, et al. “Machine Learning Streamlines the Morphometric Characterization and Multiclass Segmentation of Nuclei in Different Follicular Thyroid Lesions: Everything in a NUTSHELL.” Modern Pathology : An Official Journal of the United States and Canadian Academy of Pathology, Inc, vol. 37, no. 12, Dec. 2024, p. 100608. EBSCOhost, https://doi.org/10.1016/j.modpat.2024.100608.

APA

L’Imperio, V., Coelho, V., Cazzaniga, G., Papetti, D. M., Del Carro, F., Capitoli, G., Marino, M., Ceku, J., Fusco, N., Ivanova, M., Gianatti, A., Nobile, M. S., Galimberti, S., Besozzi, D., & Pagni, F. (2024). Machine Learning Streamlines the Morphometric Characterization and Multiclass Segmentation of Nuclei in Different Follicular Thyroid Lesions: Everything in a NUTSHELL. Modern Pathology : An Official Journal of the United States and Canadian Academy of Pathology, Inc, 37(12), 100608. https://doi.org/10.1016/j.modpat.2024.100608

Chicago

L’Imperio, Vincenzo, Vasco Coelho, Giorgio Cazzaniga, Daniele M Papetti, Fabio Del Carro, Giulia Capitoli, Mario Marino, et al. 2024. “Machine Learning Streamlines the Morphometric Characterization and Multiclass Segmentation of Nuclei in Different Follicular Thyroid Lesions: Everything in a NUTSHELL.” Modern Pathology : An Official Journal of the United States and Canadian Academy of Pathology, Inc 37 (12): 100608. doi:10.1016/j.modpat.2024.100608.