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The microscope is the mask: privileged views and labels from a cryo-ET forward model

Summary

Researchers introduce CARNIVAL, a self-supervised model for protein annotation in crowded cryo-electron tomography volumes. It uses corruptions imposed by a forward model of the microscope as paired augmented views of the same scene within the LeJEPA framework, and incorporates privileged information from the simulation pipeline—protein positions and identities—into the architecture and loss. Without fine-tuning, CARNIVAL outperforms a state-of-the-art contrastive model trained on simulated data but lacking forward-model paired views or privileged information.

SourcearXiv Computer VisionAuthor: Bogdan Toader, Kiarash Jamali, Tanmay A. M. Bharat, Sjors H. W. Scheres
The microscope is the mask: privileged views and labels from a cryo-ET forward model
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[Submitted on 3 Sep 2026]

Title:The microscope is the mask: privileged views and labels from a cryo-ET forward model

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Abstract:We explore the use of simulated data for training a model for protein annotation in crowded cryo-electron tomography volumes reconstructed from images collected at limited tilt angles and severely corrupted by the measurement operator. Firstly, we leverage the corruptions imposed by the forward model to generate domain-specific augmented paired views of the exact same scene for an invariance objective integrated into the LeJEPA self-supervised training framework. Secondly, we use additional information from the simulation pipeline such as the positions and identity of proteins in the simulated volumes to inform the architecture of the model and the loss function, so that semantic information is localised at protein positions in the resulting dense feature volume. The resulting model, CARNIVAL, is evaluated without finetuning on classification and detection tasks in real tomograms, using a benchmark dataset containing multiple protein types and two tomogram processing types. We show that CARNIVAL outperforms a state-of-the-art model trained using a contrastive objective on simulated data but without forward model-based paired views or privileged information.

Comments: 18 pages, 7 figures

Subjects:

Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Optimization and Control (math.OC)

Cite as: arXiv:2609.04325 [cs.CV]

(or arXiv:2609.04325v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2609.04325

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Bogdan Toader [view email] [v1] Thu, 3 Sep 2026 18:00:18 UTC (7,592 KB)

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Key points and analysis

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Key points

  • CARNIVAL trains on simulated cryo-ET data and uses forward-model corruptions to create domain-specific paired views for an invariance objective in LeJEPA self-supervised learning.
  • Privileged simulation labels—protein positions and identities—guide the architecture and loss function, localizing semantic information to protein sites in the dense feature volume.
  • Evaluated zero-shot on real tomograms containing multiple protein types and two processing types, CARNIVAL exceeds a contrastive SOTA baseline that lacks these inputs.

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