Explaining chest x-ray pathologies in natural language
Most deep learning algorithms lack explanations for their predictions, which limits their deployment in clinical practice. Approaches to improve explainability, especially in medical imaging, have often been shown to convey limited information, be overly reassuring, or lack robustness. In this work,...
| Main Authors: | , , , , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
Springer
2022
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