Opening the black box of machine learning in radiology: can the proximity of annotated cases be a way?

Opening the black box of machine learning in radiology: can the proximity of annotated cases be a way?

Abstract Machine learning (ML) and deep learning (DL) systems, currently employed in medical image analysis, are data-driven models often considered as black boxes. However, improved transparency is needed to translate automated decision-making to clinical practice. To this aim, we propose a strateg...

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Bibliographic Details
Main Authors: Giuseppe Baselli, Marina Codari, Francesco Sardanelli
Format: Article
Language:English
Published: SpringerOpen 2020-05-01
Series:European Radiology Experimental
Subjects:
Artificial intelligence
Decision making (computer-assisted)
Diagnosis
Machine learning
Radiology
Online Access:http://link.springer.com/article/10.1186/s41747-020-00159-0

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http://link.springer.com/article/10.1186/s41747-020-00159-0

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