Pix2Rep-v2: Data-Efficient Representation Learning for Dense Medical Imaging Applications
Dense self-supervised learning (SSL) is a powerful paradigm for learning without annotations the local descriptors required to solve dense medical imaging tasks. We present Pix2Rep-v2, a framework for SSL of pixel- and voxel-level representations suitable for few-shot downstream applications. Pix2Rep-v2 addresses the main challenges of dense SSL by leveraging a redundancy reduction objective at the pixel-level with a principle of equivariance of dense representations, that scales efficiently to 3D or wide field-of-view applications. We evaluate our method on four datasets, across multiple tasks, multiple modalities and anatomical structures using multiple backbones in 2D and 3D, and under...
| Authors | S. Sifaoui, E. Angelini, S. Toupin et al. |
|---|---|
| Primary category | cs.CV |
| Published | 1 September 2026 |
| Last updated at source | 1 September 2026 |
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