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Visual interpretability of bioimaging deep learning models

delete2024-08-09
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PRE
AI
O
Oded Rotem
A
Assaf Zaritsky *
DOI：10.1038/s41592-024-02322-6delete
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Abstract

Abstract

En 中文
The success of deep learning in analyzing bioimages comes at the expense of biologically meaningful interpretations. We review the state of the art of explainable artificial intelligence (XAI) in bioimaging and discuss its potential in hypothesis generation and data-driven discovery.
Keywords:
IMAGES

Journal

Nature Methods cover
Nature Methods
IF:
32.1
Papers:
7.2K
Citations:
12.7W

Organization

B
ben gurion university
Scholars:
1.3W
Papers: 1.0W
Citations: 5