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--- |
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license: cc |
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extra_gated_heading: "Acknowledge license to download the model weights." |
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extra_gated_description: "This project is licensed under the CC Attribution-NonCommercial 4.0 International. Our team may take 2-3 days to process your request." |
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extra_gated_button_content: "Acknowledge license" |
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--- |
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# ROSIE Model: Training and Evaluation |
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This repository contains code for training and evaluating the **ROSIE model**, designed for **H&E to multiplex protein prediction** in histopathology images. |
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## Overview |
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The ROSIE model predicts multiplex protein expression from H&E-stained histopathology images. |
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The evaluation script processes H&E images (both **ZARR** and **PNG** formats) and generates protein expression predictions across **50 channels**. |
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--- |
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## Data and Model Access |
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To request access to the pretrained model weights, accept the license terms on this page. |
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To request access to the training data, please contact alex@enablemedicine.com and aaron@enablemedicine.com. |
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## Installation |
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Install the required dependencies: |
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``` |
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pip install -r requirements.txt |
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``` |
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--- |
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## Example Usage |
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Process a single H&E image to generate multiplex protein predictions: |
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``` |
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python evaluate.py |
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--input_dir /path/to/he/images |
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--output_dir /path/to/output |
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--model_path /path/to/model.pth |
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``` |
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**Output:** |
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- A `.tiff` image file with **50 channels**. |
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- Channels correspond to the following biomarkers: |
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`DAPI, CD45, CD68, CD14, PD1, FoxP3, CD8, HLA-DR, PanCK, CD3e, CD4, aSMA, CD31, Vimentin, CD45RO, Ki67, CD20, CD11c, Podoplanin, PDL1, GranzymeB, CD38, CD141, CD21, CD163, BCL2, LAG3, EpCAM, CD44, ICOS, GATA3, Gal3, CD39, CD34, TIGIT, ECad, CD40, VISTA, HLA-A, MPO, PCNA, ATM, TP63, IFNg, Keratin8/18, IDO1, CD79a, HLA-E, CollagenIV, CD66` |
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For example: |
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- Channel 0 → DAPI |
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- Channel 1 → CD45 |
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- ... and so on. |
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**Postprocessing:** |
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Several postprocessing algorithms are available via the `--postprocess_image` flag. These adjust channel intensities for human-viewable ranges. |
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- Note: Min/max intensity depends on protein marker expression in each sample. |
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- For **quantitative analysis**, use raw output values. |
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--- |
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## Directory Structure |
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### Core Scripts |
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- **`evaluate.py`** — Evaluation and inference script. Runs inference on H&E images, predicts protein expression, outputs TIFF files. |
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- **`train.py`** — Training script implementing ConvNeXt-based architecture, patch-based training, augmentation, and evaluation. |
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- **`utils.py`** — Utility functions for image analysis, ML tasks, metrics, data loading, and visualization. |
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- **`patch_to_cell.py`** — Converts patch-level predictions into cell-level measurements using segmentation masks. |
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- **`process_exp.py`** — Processes H&E images into expression predictions and cell-level measurements. |
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- **`reconstruct_codex.py`** — Reconstructs CODEX images from parquet patch-level expression data. |
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> **Note:** Most scripts require dependencies and configurations specific to our development environment. However, `evaluate.py` can be run directly with the dependencies listed in `requirements.txt`. |
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### Configuration and Data Files |
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- **`requirements.txt`** — Python dependencies. |
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- **`Training Datasets.csv`** — Metadata for training datasets. |
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- **`Antibody Information.xlsx`** — Antibody and biomarker details for the 50 protein channels. |
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--- |
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## Citation |
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If you use this repository in your research, please cite: |
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Wu, Eric, et al. "ROSIE: AI generation of multiplex immunofluorescence staining from histopathology images." Nature Communications 2025. |
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👉 [ROSIE, Nature Communications 2025](https://www.nature.com/articles/s41467-025-62346-0) |
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--- |
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## License |
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This project is licensed under the **CC Attribution-NonCommercial 4.0 International**. |
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See the [LICENSE](LICENSE) file for details. |
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--- |
