Image annotation and curation in radiology: an overview for machine learning practitioners
Abstract “Garbage in, garbage out” summarises well the importance of high-quality data in machine learning and artificial intelligence. All data used to train and validate models should indeed be consistent, standardised, traceable, correctly annotated, and de-identified, considering local regulatio...
Main Authors: | Fabio Galbusera, Andrea Cina |
---|---|
Format: | Article |
Language: | English |
Published: |
SpringerOpen
2024-02-01
|
Series: | European Radiology Experimental |
Subjects: | |
Online Access: | https://doi.org/10.1186/s41747-023-00408-y |
Similar Items
-
Implementation of a Large-Scale Image Curation Workflow Using Deep Learning Framework
by: Amitha Domalpally, MD, PhD, et al.
Published: (2022-12-01) -
Opening the black box of machine learning in radiology: can the proximity of annotated cases be a way?
by: Giuseppe Baselli, et al.
Published: (2020-05-01) -
AI-Based Analysis of Policies and Images for Privacy-Conscious Content Sharing
by: Francesco Contu, et al.
Published: (2021-05-01) -
General intelligence requires rethinking exploration
by: Minqi Jiang, et al.
Published: (2023-06-01) -
Creating Guidance for Canadian Dataverse Curators: Portage Network’s Dataverse Curation Guide
by: Alexandra Cooper, et al.
Published: (2021-08-01)