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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2020-05-01
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| Series: | European Radiology Experimental |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s41747-020-00159-0 |