  This paper introduces vox2vec - a contrastive method for self-supervised
learning (SSL) of voxel-level representations. vox2vec representations are
modeled by a Feature Pyramid Network (FPN): a voxel representation is a
concatenation of the corresponding feature vectors from different pyramid
levels. The FPN is pre-trained to produce similar representations for the same
voxel in different augmented contexts and distinctive representations for
different voxels. This results in unified multi-scale representations that
capture both global semantics (e.g., body part) and local semantics (e.g.,
different small organs or healthy versus tumor tissue). We use vox2vec to
pre-train a FPN on more than 6500 publicly available computed tomography
images. We evaluate the pre-trained representations by attaching simple heads
on top of them and training the resulting models for 22 segmentation tasks. We
show that vox2vec outperforms existing medical imaging SSL techniques in three
evaluation setups: linear and non-linear probing and end-to-end fine-tuning.
Moreover, a non-linear head trained on top of the frozen vox2vec
representations achieves competitive performance with the FPN trained from
scratch while having 50 times fewer trainable parameters. The code is available
at https://github.com/mishgon/vox2vec .
