Loki2

Loki2 is a single-cell pathology foundation model that uses an encoder–decoder architecture to reliably translate nucleus morphology into molecular-level information. Loki2 is trained on UniSeg, a pan-tissue resource of 15 million cells with paired segmentation and transcriptomic measurements from multiple spatial technologies. This training strategy enables three capabilities from hematoxylin and eosin (H&E) images alone: universal cell type inference, in silico spatial transcriptomics by retrieving transcriptionally matched cells from reference atlases, and continuous morphological pseudotime inference of cell state transitions. By further aggregating single-cell features, Loki2 supports in silico protein staining and whole-slide clinical inference. These results show that routine histology contains far richer molecular and cellular information than previously recognized, and that Loki2 provides a general framework for accessing this information across technologies, tissues, disease contexts, and scales.

The pre-train weights and source code will be released on GitHub and Hugging Face after the manuscript is accepted.

User Manual and Notebooks

You can view the Loki2 website and notebooks here. This README provides a quick overview of how to set up and use Loki2.

Source Code

All source code for Loki2 is contained in the ./src/loki2 directory.

The source code will be released on GitHub and Hugging Face after the manuscript is accepted.

Project Structure

Please organize your project folders as follows:

.
├── src/           # Source code for Loki2
├── model_ckpt/    # Pretrained Loki2 model weights
├── data/          # Input data files
├── notebooks/     # Jupyter notebooks for tutorials and examples
└── outputs/       # Output files and results

Installation

1. Create a conda environment:

   conda env create -f environment.yaml
   conda activate loki2_env

2. Navigate to the Loki source directory and install Loki2:

   cd ./src
   pip install .

Usage

Once Loki2 is installed, you can import it in your Python scripts or notebooks:

import loki2.preprocess
import loki2.plot
import loki2.retrieve
import loki2.psdtime
import loki2.immstain
import loki2.mil

Pretrained weights

The pretrained weights will be released on GitHub and Hugging Face after the manuscript is accepted.