Update README.md

README.md

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-license: cc-by-nc-nd-4.0
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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">
+
+[![arXiv](https://img.shields.io/badge/arXiv-2601.21560-b31b1b.svg)](https://arxiv.org/abs/2601.21560)
+[![Model Architecture](https://img.shields.io/badge/Model-Transformer-blue)]()
+[![Dataset](https://img.shields.io/badge/Dataset-HEST-green)]()
+
+</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"])