  For more than a decade, deep learning models have been dominating in various
2D imaging tasks. Their application is now extending to 3D imaging, with 3D
Convolutional Neural Networks (3D CNNs) being able to process LIDAR, MRI, and
CT scans, with significant implications for fields such as autonomous driving
and medical imaging. In these critical settings, explaining the model's
decisions is fundamental. Despite recent advances in Explainable Artificial
Intelligence, however, little effort has been devoted to explaining 3D CNNs,
and many works explain these models via inadequate extensions of 2D saliency
methods.
  A fundamental limitation to the development of 3D saliency methods is the
lack of a benchmark to quantitatively assess these on 3D data. To address this
issue, we propose SE3D: a framework for Saliency method Evaluation in 3D
imaging. We propose modifications to ShapeNet, ScanNet, and BraTS datasets, and
evaluation metrics to assess saliency methods for 3D CNNs. We evaluate both
state-of-the-art saliency methods designed for 3D data and extensions of
popular 2D saliency methods to 3D. Our experiments show that 3D saliency
methods do not provide explanations of sufficient quality, and that there is
margin for future improvements and safer applications of 3D CNNs in critical
fields.
