SOURCE-LINKED INTELLIGENCE

Pix2Rep-v2: Data-Efficient Representation Learning for Dense Medical Imaging Applications

arXiv · AI, language, vision and robotics · article · Sep 1, 2026 · UTC

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 task

Read original source ↗ Open in workspace

recordType

paper

region

Global