Visual_Interpretability_of_Bioimaging_Deep_Learning_Models__neurips_
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.
Cite
Text
Rotem. "Visual_Interpretability_of_Bioimaging_Deep_Learning_Models__neurips_." NeurIPS 2024 Workshops: AIM-FM, 2024.Markdown
[Rotem. "Visual_Interpretability_of_Bioimaging_Deep_Learning_Models__neurips_." NeurIPS 2024 Workshops: AIM-FM, 2024.](https://mlanthology.org/neuripsw/2024/rotem2024neuripsw-visual/)BibTeX
@inproceedings{rotem2024neuripsw-visual,
title = {{Visual_Interpretability_of_Bioimaging_Deep_Learning_Models__neurips_}},
author = {Rotem, Oded},
booktitle = {NeurIPS 2024 Workshops: AIM-FM},
year = {2024},
url = {https://mlanthology.org/neuripsw/2024/rotem2024neuripsw-visual/}
}