  Training Artificial Intelligence (AI) models on 3D images presents unique
challenges compared to the 2D case: Firstly, the demand for computational
resources is significantly higher, and secondly, the availability of large
datasets for pre-training is often limited, impeding training success. This
study proposes a simple approach of adapting 2D networks with an intermediate
feature representation for processing 3D images. Our method employs attention
pooling to learn to assign each slice an importance weight and, by that, obtain
a weighted average of all 2D slices. These weights directly quantify the
contribution of each slice to the contribution and thus make the model
prediction inspectable. We show on all 3D MedMNIST datasets as benchmark and
two real-world datasets consisting of several hundred high-resolution CT or MRI
scans that our approach performs on par with existing methods. Furthermore, we
compare the in-built interpretability of our approach to HiResCam, a
state-of-the-art retrospective interpretability approach.
