14 citations · 14 across the 2 of their papers we have counts for
2 papers
eess.IV2025
Development of a fully deep learning model to improve the reproducibility of sector classification systems for predicting unerupted maxillary canine likelihood of impaction
Marzio Galdi, Davide Cannatà, Flavia Celentano +4
Objectives. The aim of the present study was to develop a fully deep learning model to reduce the intra- and inter-operator reproducibility of sector classification systems for pre…
cs.LG2020★ 14 cited
Classification of cancer pathology reports: a large-scale comparative study
Stefano Martina, Leonardo Ventura, Paolo Frasconi
We report about the application of state-of-the-art deep learning techniques to the automatic and interpretable assignment of ICD-O3 topography and morphology codes to free-text ca…