Few-Shot Conditional Learning: Automatic and Reliable Device Classification for Medical Test Equipment

Autor: Eva Pachetti, Giulio Del Corso, Serena Bardelli, Sara Colantonio

Publikováno v: Journal of Imaging, Vol 10, Iss 7, p 167 (2024)

The limited availability of specialized image databases (particularly in hospitals, where tools vary between providers) makes it difficult to train deep learning models. This paper presents a few-shot learning methodology that uses a pre-trained ResN

Externí odkaz: https://doaj.org/article/8adcf0b6289f4d6398f0ecc2c531536a

Effectiveness of a new sensorized videolaryngoscope for retraining on neonatal intubation in simulation environment

Autor: Alice Covelli, Serena Bardelli, Rosa T. Scaramuzzo, Emilio Sigali, Massimiliano Ciantelli, Marta Del Pistoia, Alessia Longo, Selene Tognarelli, Arianna Menciassi, Armando Cuttano

Publikováno v: Italian Journal of Pediatrics, Vol 46, Iss 1, Pp 1-11 (2020)

Abstract Background In recent years, medical training has significantly increased the use of simulation for teaching and evaluation. The retraining of medical personnel in Italy is entrusted to the program of Continuous Education in Medicine, mainly

Externí odkaz: https://doaj.org/article/836abbb168084f9081817aa871991a31

Automatic detection of potentially ineffective verbal communication for training through simulation in neonatology

Autor: Gianpaolo Coro, Serena Bardelli, Armando Cuttano, Nicoletta Fossati

Publikováno v: Education and information technologies (Dordr., Online) (2022). doi:10.1007/s10639-022-11000-z
info:cnr-pdr/source/autori:Coro G.; Bardelli S.; Cuttano A.; Fossati N./titolo:Automatic detection of potentially ineffective verbal communication for training through simulation in neonatology/doi:10.1007%2Fs10639-022-11000-z/rivista:Education and information technologies (Dordr., Online)/anno:2022/pagina_da:/pagina_a:/intervallo_pagine:/volume

Training through simulation in neonatology relies on sophisticated simulation devices that give realistic feedback to trainees during simulated scenarios. It aims at training highly specialised medical teams in established operational skills, timely

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1fdc0d5b144006789e173f0f765e9239
https://openportal.isti.cnr.it/doc?id=people______::e2c4a36202faacf683aebd94aa79e532