Visual interpretability of bioimaging deep learning models

Oded Rotem; Assaf Zaritsky

doi:10.1038/s41592-024-02322-6

Visual interpretability of bioimaging deep learning models

Oded Rotem
, Assaf Zaritsky

Research output: Contribution to journal › Comment/debate

13 Scopus citations

Abstract

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.

Original language	English
Pages (from-to)	1394-1397
Number of pages	4
Journal	Nature Methods
Volume	21
Issue number	8
DOIs	https://doi.org/10.1038/s41592-024-02322-6
State	Published - 1 Aug 2024

ASJC Scopus subject areas

Biotechnology
Biochemistry
Molecular Biology
Cell Biology

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title = "Visual interpretability of bioimaging deep learning models",

abstract = "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.",

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AU - Rotem, Oded

AU - Zaritsky, Assaf

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Y1 - 2024/8/1

N2 - 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.

AB - 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.

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DO - 10.1038/s41592-024-02322-6

M3 - Comment/debate

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JO - Nature Methods

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Visual interpretability of bioimaging deep learning models

Abstract

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