| --- |
| language: en |
| license: apache-2.0 |
| source_code: https://github.com/pwesp/sail |
| tags: |
| - sparse-autoencoder |
| - matryoshka |
| - ct |
| - mri |
| --- |
| |
| # SAIL — Pretrained SAE Weights |
|
|
| Pretrained Matryoshka Sparse Autoencoder (SAE) weights for the [SAIL](https://github.com/pwesp/sail) repository. See the project page for the full pipeline and usage instructions. |
|
|
| Two checkpoints are provided, one for each foundation model (FM) embedding space: |
|
|
| | File | Foundation model | Input dim | Dictionary sizes | k values | |
| |------|-----------------|-----------|-----------------|----------| |
| | `biomedparse_sae.ckpt` | BiomedParse | 1536 | 128, 512, 2048, 8192 | 20, 40, 80, 160 | |
| | `dinov3_sae.ckpt` | DINOv3 | 1024 | 128, 512, 2048, 8192 | 5, 10, 20, 40 | |
|
|
| Both SAEs were trained on CT and MRI embeddings from the [TotalSegmentator](https://github.com/wasserth/TotalSegmentator) dataset. |
|
|
| ## Usage |
|
|
| To download these weights and place them in the expected directory structure, run from the repo root: |
|
|
| ```bash |
| bash pretrained/download_weights.sh |
| ``` |
|
|
| ## Citation |
|
|
| If you find this work useful, please cite our paper: |
|
|
| ```bibtex |
| @misc{sail2026, |
| title = {Sparse Autoencoders for Interpretable Medical Image Representation Learning}, |
| author = {Wesp, Philipp and Holland, Robbie and Sideri-Lampretsa, Vasiliki and Gatidis, Sergios}, |
| year = 2026, |
| journal = {arXiv.org}, |
| howpublished = {https://arxiv.org/abs/2603.23794v1} |
| } |
| ``` |
