---
license: bsd-2-clause
---
# OCTCube Models Release

This repository hosts the official release of **OCTCube-M: A 3D multimodal optical coherence tomography foundation model for retinal and systemic diseases with cross-cohort and cross-device validation**: [[Github repo](https://github.com/ZucksLiu/OCTCubeM)] | [[Arxiv](https://arxiv.org/abs/2408.11227)].

We release **OCTCube**, **OCTCube-IR**, and a **Multi-task Classification Model** for 8 retinal diseases. These models are designed for **multi-modal OCT analysis** and support **image classification tasks** in ophthalmology.

## 🔹 Available Models

| Model Name | Description |
|------------|-------------|
| OCTCube | A foundation model trained on OCT data for learning shared embeddings. |
| OCTCube-IR | An infrared-enhanced version of OCTCube that integrates IR modality. |
| Multi-task Classification Model | A model trained for multi-disease classification of 8 retinal diseases. |



## 📜 Model Details
- **OCTCube**: Designed for learning joint representations of OCT images.
- **OCTCube-IR**: Extends OCTCube by incorporating IR modality for improved alignment.
- **Multi-task Classification Model**: A specialized classification model for **8 retinal diseases**, leveraging multi-task learning.

## 📂 Example Data
- `oct_examples/`: Example OCT data volumes provided for trying our inference notebook! Includes:
  - `AMD.dcm`
  - `DR.dcm`
  - `POAG.dcm`
  - `DME.dcm`
  - `RNV.dcm`
  - `VD.dcm`
  - `ERM.dcm`
  - `CRAO/CRVO.dcm`


## 🔧 Usage

To use the models in **PyTorch**, please check the [inference noteboook](https://github.com/ZucksLiu/OCTCubeM/blob/main/inference_OCTCube.ipynb).




## 📑 Citation
If you use these models in your research, please cite:
```
@article{liu2024octcube,
  title={OCTCube: a 3D foundation model for optical coherence tomography that improves cross-dataset, cross-disease, cross-device and cross-modality analysis},
  author={Liu, Zixuan and Xu, Hanwen and Woicik, Addie and Shapiro, Linda G and Blazes, Marian and Wu, Yue and Lee, Cecilia S and Lee, Aaron Y and Wang, Sheng},
  journal={arXiv preprint arXiv:2408.11227},
  year={2024}
}
```



## 💬 Contact
For any questions or feedback, feel free to open an issue or contact us directly (**zucksliu@cs.washington.edu**).

---

🚀 Stay tuned for updates and improvements!