README.md

---
license: cc-by-nc-nd-4.0
tags:
- spatial-transcriptomics
- pathology
- histology
- deep-learning
- pytorch
---

# MoLF: Mixture-of-Latent-Flow for Pan-Cancer Spatial Gene Expression Prediction from Histology

<div align="center">

[![arXiv](https://img.shields.io/badge/arXiv-2601.21560-b31b1b.svg)](https://arxiv.org/abs/2602.02282)
[![Model Architecture](https://img.shields.io/badge/Model-MoLF-blue)]()
[![Dataset](https://img.shields.io/badge/Dataset-HEST-green)]()

</div>

**MoLF** 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/MoLF).

This repository contains the weights for the checkpoints in the paper trained on the HEST v1.1.0 dataset.

## 📄 Paper
**Title:** MoLF: Mixture-of-Latent-Flow for Pan-Cancer Spatial Gene Expression Prediction from Histology  
**Authors:** Hu, Susu and Speidel, Stefanie  
**Link:** [ICLR 2026 arXiv](https://arxiv.org/abs/2602.02282)


## 💻 Usage
To load this checkpoint, ensure you have the MoLF 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/MoLF", filename=ckpt/latent_flow/latent_flow_split_0.pt")

# 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"])