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This paper introduces vox2vec - a contrastive method for self-supervisedlearning (SSL) of voxel-level representations. vox2vec representations aremodeled by a Feature Pyramid Network (FPN): a voxel representation is aconcatenation of the corresponding feature vectors from different pyramidlevels. The FPN is pre-trained to produce similar representations for the samevoxel in different augmented contexts and distinctive representations fordifferent voxels. This results in unified multi-scale representations thatcapture both global semantics (e.g., body part) and local semantics (e.g.,different small organs or healthy versus tumor tissue). We use vox2vec topre-train a FPN on more than 6500 publicly available computed tomographyimages. We evaluate the pre-trained representations by attaching simple headson top of them and training the resulting models for 22 segmentation tasks. Weshow that vox2vec outperforms existing medical imaging SSL techniques in threeevaluation setups: linear and non-linear probing and end-to-end fine-tuning.Moreover, a non-linear head trained on top of the frozen vox2vecrepresentations achieves competitive performance with the FPN trained fromscratch while having 50 times fewer trainable parameters. The code is availableat https://github.com/mishgon/vox2vec .

vox2vec: A Framework for Self-supervised Contrastive Learning of Voxel-level Representations in Medical Images