arXiv Machine Learning October 22, 2026 By Sonia Laguna, Andrea Agostini, Alain Ryser, Samuel Ruiperez-Campillo, Irene Cannistraci, Moritz Vandenhirtz, Stephan Mandt, Nicolas Deperrois, Farhad Nooralahzadeh, Michael Krauthammer, Thomas M. Sutter, Julia E. Vogt

Structure is Supervision: Multiview Masked Autoencoders for Radiology

Read the original on arXiv Machine Learning →

The paper introduces Multiview Masked Autoencoder (MVMAE), a self‑supervised framework that uses the natural multi‑view structure of radiology studies to learn view‑invariant, disease‑relevant representations by combining masked image reconstruction with cross‑view alignment. An extended version, MVMAE‑V2T, adds radiology reports as an auxiliary text signal to improve semantic grounding while keeping inference vision‑only. Experiments on MIMIC‑CXR, CheXpert, and PadChest show that MVMAE outperforms supervised and vision‑language baselines, and MVMAE‑V2T yields further gains, especially when labeled data are scarce.

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