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

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.

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

💻 Usage

To load this checkpoint, ensure you have the MoLF codebase or compatible model definition.

from huggingface_hub import hf_hub_download
import torch

# Download the model checkpoint
checkpoint_path = hf_hub_download(repo_id="HuSusu/MoLF", filename="MoLF_latent_vae.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"])