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license: cc-by-nc-nd-4.0
tags:
- spatial-transcriptomics
- pathology
- histology
- deep-learning
- pytorch
---
# HistoPrism: Unlocking Functional Pathway Analysis from Pan-Cancer Histology via Gene Expression Prediction
<div align="center">
[](https://arxiv.org/abs/2601.21560)
[]()
[]()
</div>
**HistoPrism** is a deep learning model designed to bridge the gap between histology images (H&E) and spatial gene expression.
Check out the details in the [github repo](https://github.com/susuhu/HistoPrism).
This repository contains the weights for the checkpoints in the paper trained on the HEST v1.1.0 dataset.
## 📄 Paper
**Title:** HistoPrism: Unlocking Functional Pathway Analysis from Pan-Cancer Histology via Gene Expression Prediction
**Authors:** Hu, Susu and Zeng, Qinghe and Bhasker, Nithya and Kather, Jakob Nicholas and Speidel, Stefanie
**Link:** [ICLR 2026 arXiv](https://arxiv.org/abs/2601.21560)
## 💻 Usage
To load this checkpoint, ensure you have the HistoPrism codebase or compatible model definition.
```python
from huggingface_hub import hf_hub_download
import torch
# Download the model checkpoint
checkpoint_path = hf_hub_download(repo_id="HuSusu/HistoPrism", filename="HistoPrism_split0.ckpt")
# Load weights (Pseudo-code: replace with your actual model class)
# model = HistoPrism(config=...)
# checkpoint = torch.load(path, map_location=map_location)
# model.load_state_dict(checkpoint["model_state"])
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