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CIPHER

Feature Extraction
PyTorch
computational-pathology
spatial-proteomics
histopathology
colorectal-cancer
multimodal
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metadata 
library_name: pytorch
pipeline_tag: feature-extraction
tags:
  - computational-pathology
  - spatial-proteomics
  - histopathology
  - colorectal-cancer
  - multimodal

CIPHER model weights

This repository contains the released weights for CIPHER (Cross-Panel Integration of Proteomics for Histopathology-based Ecosystem Representation). CIPHER learns protein-defined tissue representations from spatial proteomics and transfers them to routine H&E images.

The implementation and tutorials are maintained at https://github.com/MedCAI/CIPHER-public.

CIPHER is research software and has not been validated as a clinical decision-support system.

Files

File Purpose Size
cipher_sp/cipher_sp_weights.pt CIPHER-SP spatial-proteomics checkpoint 1,474,240,075 bytes
cipher_he/cipher_he_lora.pt CIPHER-HE LoRA adapter 9,992,877 bytes
metadata/share_schema.json Shared CODEX/ORION channel schema for CIPHER-SP 1,761 bytes

Download

Install huggingface_hub, then download both weights into the layout expected by the GitHub repository:

python -m pip install --upgrade huggingface_hub
hf download Ceilinghans/CIPHER cipher_sp/cipher_sp_weights.pt cipher_he/cipher_he_lora.pt --local-dir artifacts/models

Or download individual files from Python:

from huggingface_hub import hf_hub_download

sp_checkpoint = hf_hub_download(
    repo_id="Ceilinghans/CIPHER",
    filename="cipher_sp/cipher_sp_weights.pt",
)
he_adapter = hf_hub_download(
    repo_id="Ceilinghans/CIPHER",
    filename="cipher_he/cipher_he_lora.pt",
)

CIPHER-SP inference

Install the CIPHER-SP package from the code repository, prepare a manifest as described in CIPHER-SP/README.md, and run:

python CIPHER-SP/extract_features.py \
  --checkpoint artifacts/models/cipher_sp/cipher_sp_weights.pt \
  --manifest CIPHER-SP/metadata/manifest.csv \
  --schema CIPHER-SP/metadata/share_schema.json \
  --out_pt outputs/patch_features.pt \
  --feature_type mean_patch \
  --device cuda

The input TIFF patches must have shape channels x 224 x 224, with channels ordered according to the schemas in the code repository.

CIPHER-HE inference

CIPHER-HE is a LoRA adapter and does not include the UNI2-h backbone. Obtain UNI2-h base weights separately under their original access and license terms, then run:

cipher-he /path/to/he_patches --recursive \
  --output outputs/features.pt \
  --feature-type projected \
  --checkpoint artifacts/models/cipher_he/cipher_he_lora.pt \
  --base-weights /path/to/UNI2-h/pytorch_model.bin

Use --feature-type projected for normalized 512-dimensional cross-modal features or --feature-type backbone for 1,536-dimensional adapted UNI2-h features.

Provenance and limitations

  • The CIPHER-HE file is the epoch-10 LoRA training checkpoint. Optimizer state, environment-specific filesystem paths and cohort-split identifiers were removed without changing the trainable tensor values.
  • Exact manuscript-training provenance of the CIPHER-SP checkpoint is not verified. Its embedded metadata reports epoch 9 and a 30-epoch, batch-size-128 recipe, whereas the manuscript describes 50 epochs and batch size 256. The audited implementation also uses one shared marker-decoder parameter set for both platforms. Do not present this checkpoint as an exact reproduction of manuscript training.
  • Neither checkpoint contains patient, slide, patch or image data.
  • These are PyTorch .pt files. PyTorch checkpoints may use pickle-based deserialization; download them only from the official repository.

Citation

If you use these weights, please cite the accompanying manuscript:

Spatial proteomics-informed histopathology identifies prognostic tissue ecosystems in colorectal cancer.

Formal citation details will be added after publication.