Many recent approaches in representation learning implicitly assume that uncorrelated views of a data point are sufficient to learn meaningful representations for various downstream tasks. In this work, we challenge this assumption and demonstrate that meaningful structure in the latent space does not emerge naturally. Instead, it must be explicitly induced. We propose a method that aligns representations from different views of the data to align complementary information without inducing false positives. Our experiments show that our proposed self-supervised learning method, Consistent View Alignment, improves performance for downstream tasks, highlighting the critical role of structured view alignment in learning effective representations. Our method achieved first and second place in the MICCAI 2025 SSL3D challenge when using a Primus vision transformer and ResEnc convolutional neural network, respectively. The code and pretrained model weights are released at https://github.com/Tenbatsu24/LatentCampus.
@article{arxiv.2509.13846,
title = {Consistent View Alignment Improves Foundation Models for 3D Medical Image Segmentation},
author = {Puru Vaish and Felix Meister and Tobias Heimann and Christoph Brune and Jelmer M. Wolterink},
journal= {arXiv preprint arXiv:2509.13846},
year = {2025}
}
Comments
MICCAI 2025: 1st Place in Transformer track and 2nd Place in Convolution track of SSL3D-OpenMind challenge