Methods › Computer Vision › 3D Representations › Models Genesis
Models Genesis
Introduced by Zongwei Zhou et al. in Models Genesis
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Models Genesis, or Generic Autodidactic Models, is a self-supervised approach for learning 3D image representations. The objective of Models Genesis is to learn a common image representation that is transferable and generalizable across diseases, organs, and modalities. It consists of an encoder-decoder architecture with skip connections in between, and is trained to learn a common image representation by restoring the original sub-volume xᵢ (as ground truth) from the transformed one x̅ᵢ (as input), in which the reconstruction loss (MSE) is computed between the model prediction x′₀ and ground truth xᵢ. Once trained, the encoder alone can be fine-tuned for target classification tasks; while the encoder and decoder together can be fine-tuned for target segmentation tasks.
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Models Genesis 9 Apr 2020 · 2 repositories · arXiv:2004.07882
Tasks archive 2025-07-28
4 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Anatomy | 1 |
| Medical Image Analysis | 1 |
| Self-Supervised Learning | 1 |
| Transfer Learning | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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