Histoscope UNI/SPIDER TopK SAE
This repository contains the fixed sparse autoencoder (SAE) checkpoint used in Histoscope: Expert-Grounded Inspection of Sparse Autoencoder Features in Histopathology Foundation Models.
- Paper: https://openreview.net/forum?id=UIqw2I1CQk
- Histoscope source: https://github.com/mnhcorp/histoscope
- SPIDER-colorectal: https://huggingface.co/datasets/histai/SPIDER-colorectal
- UNI encoder: https://huggingface.co/MahmoodLab/UNI
The SAE was trained on 1,024-dimensional frozen UNI embeddings from the SPIDER-colorectal training split. It maps each embedding to a 49,152-feature dictionary and retains the largest 250 feature activations per patch.
Files
| File | Description |
|---|---|
model.pt | PyTorch state dictionary used in the paper |
config.json | Architecture, training, preprocessing, and recorded metrics |
metadata.json | Original training receipt emitted with the checkpoint |
SHA256SUMS | Integrity checksum |
The state dictionary contains two tensors:
| Key | Shape | Meaning |
|---|---|---|
encoder.weight | 49152 x 1024 | Tied feature directions |
b_pre | 1024 | Learned pre-encoder bias |
The decoder uses encoder.weight.T; there is no separate decoder tensor.
Download
hf download mnhcorp/histoscope-uni-spider-sae \
--local-dir ./histoscope-uni-spider-sae
The checkpoint can then be placed in the model directory expected by
Histoscope. See the repository's
models/README.md
for the required dashboard-cache layout.
Configuration
- Input dimension: 1,024
- Dictionary size: 49,152 (48x expansion)
- Activation: per-patch TopK,
k=250 - Weights: tied encoder/decoder
- Pre-encoder bias: enabled
- Training: 2 epochs, batch size 32, learning rate
1e-4, seed 42 - Preprocessing: z-score normalization followed by row-wise L2 normalization
- Recorded reconstruction loss:
1.3156334171071649e-4 - Recorded mean active features per patch:
250
The corrected release pipeline fits normalization statistics on the training
split and applies them unchanged to held-out embeddings. Refer to config.json
and the Histoscope pipeline documentation for the executable receipt.
Intended use
This checkpoint is a derivative research model trained on embeddings produced
by UNI. It is released solely for non-commercial academic research under
CC BY-NC-ND 4.0 and remains subject to the UNI and SPIDER-colorectal upstream
terms. It is not a diagnostic model or a medical device and must not be used for
clinical decision-making.
The checkpoint does not contain source images, patient data, UNI weights, UNI embeddings, or pathologist responses. To reproduce the complete experiment, users must separately obtain access to SPIDER-colorectal and UNI under their respective terms, generate UNI embeddings, and run the released analysis pipeline.
License and upstream terms
This SAE checkpoint inherits UNI's CC BY-NC-ND 4.0 license and was trained on
SPIDER-colorectal data. The upstream terms therefore apply in addition to the
separate Histoscope source-code license:
- UNI is distributed under
CC BY-NC-ND 4.0with additional gated-access terms covering models trained on UNI outputs. - SPIDER-colorectal is distributed under
CC BY-NC 4.0for research use. - Histoscope source code is separately released under GPL-3.0.
Use of this checkpoint is limited to non-commercial academic research with proper attribution. Users are responsible for complying with all applicable upstream restrictions. This repository does not redistribute UNI weights, UNI embeddings, SPIDER-colorectal images, or pathologist responses, and it does not grant rights to redistribute those resources beyond their upstream terms.
Citation
@inproceedings{hossain2026histoscope,
title = {Histoscope: Expert-Grounded Inspection of Sparse Autoencoder Features in Histopathology Foundation Models},
author = {Hossain, Mirza Nasir and Bell, Sarah L. and Bryson, Gareth and Harris-Birtill, David},
booktitle = {Mechanistic Interpretability Workshop at the 43rd International Conference on Machine Learning},
year = {2026},
url = {https://openreview.net/forum?id=UIqw2I1CQk}
}