PAPETTI DANIELE MARIA

Monza, N., Porto, N., Coelho, V., L’Imperio, V., Papetti, D., Di Nicoli, F., et al. (2025). Enhancing proteomics characterization of thyroid nodules: a pixel-classifier for mass spectrometry imaging analyses. Intervento presentato a: 12th MS J-day, Scuola Normale di Pisa, Pisa, Italia. Dettaglio

Bacciu, L., Urso, M., Coelho, V., Cazzaniga, G., Pincelli, A., Garancini, M., et al. (2025). MiThyCA: A Computational Pathology Pipeline for the Identification of Microscopic Foci of Papillary Thyroid Carcinoma-Like Nuclear Features with AI in Whole-Slide Histological Images. ENDOCRINE PATHOLOGY, 36(1) [10.1007/s12022-025-09877-w]. Dettaglio

Papetti, D., Tangherloni, A., Coelho, V., Besozzi, D., Cazzaniga, P., Nobile, M. (2025). We Are Sending You Back... to the Optimum! Fuzzy Time Travel Particle Swarm Optimization. In Applications of Evolutionary Computation 28th European Conference, EvoApplications 2025, Held as Part of EvoStar 2025, Trieste, Italy, April 23–25, 2025, Proceedings, Part II (pp.160-175). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-031-90065-5_10]. Dettaglio

(2024). Meta-problems in global optimization: new perspectives from Computational Intelligence. (Tesi di dottorato, Università degli Studi di Milano-Bicocca, 2024). Dettaglio

L'Imperio, V., Coelho, V., Cazzaniga, G., Papetti, D., Del Carro, F., Capitoli, G., et al. (2024). Machine learning streamlines the morphometric characterization and multi-class segmentation of nuclei in different follicular thyroid lesions: everything in a NUTSHELL. MODERN PATHOLOGY, 37(12 (December 2024)) [10.1016/j.modpat.2024.100608]. Dettaglio