  The automatic classification of 3D medical data is memory-intensive. Also,
variations in the number of slices between samples is common. Na\"ive solutions
such as subsampling can solve these problems, but at the cost of potentially
eliminating relevant diagnosis information. Transformers have shown promising
performance for sequential data analysis. However, their application for long
sequences is data, computationally, and memory demanding. In this paper, we
propose an end-to-end Transformer-based framework that allows to classify
volumetric data of variable length in an efficient fashion. Particularly, by
randomizing the input volume-wise resolution(#slices) during training, we
enhance the capacity of the learnable positional embedding assigned to each
volume slice. Consequently, the accumulated positional information in each
positional embedding can be generalized to the neighbouring slices, even for
high-resolution volumes at the test time. By doing so, the model will be more
robust to variable volume length and amenable to different computational
budgets. We evaluated the proposed approach in retinal OCT volume
classification and achieved 21.96% average improvement in balanced accuracy on
a 9-class diagnostic task, compared to state-of-the-art video transformers. Our
findings show that varying the volume-wise resolution of the input during
training results in more informative volume representation as compared to
training with fixed number of slices per volume.
