Quality assurance for automatically generated contours with additional deep learning

Quality assurance for automatically generated contours with additional deep learning

Abstract Objective Deploying an automatic segmentation model in practice should require rigorous quality assurance (QA) and continuous monitoring of the model’s use and performance, particularly in high-stakes scenarios such as healthcare. Currently, however, tools to assist with QA for such models...

Full description

Bibliographic Details
Main Authors: Lars Johannes Isaksson, Paul Summers, Abhir Bhalerao, Sara Gandini, Sara Raimondi, Matteo Pepa, Mattia Zaffaroni, Giulia Corrao, Giovanni Carlo Mazzola, Marco Rotondi, Giuliana Lo Presti, Zaharudin Haron, Sara Alessi, Paola Pricolo, Francesco Alessandro Mistretta, Stefano Luzzago, Federica Cattani, Gennaro Musi, Ottavio De Cobelli, Marta Cremonesi, Roberto Orecchia, Giulia Marvaso, Giuseppe Petralia, Barbara Alicja Jereczek-Fossa
Format: Article
Language:English
Published: SpringerOpen 2022-08-01
Series:Insights into Imaging
Subjects:
Quality assurance (Health care)
Confidence calibration
Diagnostic imaging
Prostate
Magnetic resonance imaging
Online Access:https://doi.org/10.1186/s13244-022-01276-7

Internet

https://doi.org/10.1186/s13244-022-01276-7

Similar Items