A clinical deep learning framework for continually learning from cardiac signals across diseases, time, modalities, and institutions.
Deep learning algorithms trained on instances that violate the assumption of being independent and identically distributed (i.i.d.) are known to experience destructive interference, a phenomenon characterized by a degradation in performance. Such a violation, however, is ubiquitous in clinical setti...
| Main Authors: | , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Springer Nature
2021
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