| --- |
| base_model: |
| - stabilityai/stable-diffusion-xl-base-1.0 |
|
|
| license: apache-2.0 |
| language: |
| - en |
|
|
| tags: |
| - cytology |
| - pathology |
| - medical-imaging |
| - diffusion-model |
| - image-generation |
| - text-to-image |
|
|
| library_name: diffusers |
| pipeline_tag: text-to-image |
| --- |
| |
|
|
| # Model Card for **COIN** |
|
|
|
|
| ## What is COIN? |
|
|
|
|
| **COIN (Cytology generative fOundatIoN model)** is a controllable foundation model for cytology image generation, developed to address the long-standing challenges of **data scarcity** and **privacy constraints** in computational cytology. |
|
|
| COIN is trained on 112,226 cytology image–report pairs from 16 anatomical sites, enabling it to generate high-fidelity, text-controllable cytology images that preserve both morphological and diagnostic realism. |
|
|
| It supports a wide range of downstream applications, including AI model data augmentation, diagnostic model pretraining, and content-based image retrieval, making it the first foundation model to provide scalable synthetic data generation for cytopathology. |
|
|
|
|
| ## Usage |
|
|
| Install the conch repository using pip: |
|
|
| ``` |
| |
| pip install git+https://github.com/LexieK7/COIN.git |
| |
| ``` |
|
|
| After succesfully requesting access to the weights: |
|
|
| ``` |
| |
| from diffusers import DiffusionPipeline |
| import torch |
| import os |
| |
| sdxl_base_model = "./sd_xl_1-0" |
| lora_model_path = "MODEL PATH" |
| save_folder = "./generated_images" |
| prompt = "No intraepithelial lesion or malignancy (NILM)." |
| guidance_scale = 7.5 |
| num_inference_steps = 50 |
| |
| pipe = DiffusionPipeline.from_pretrained(sdxl_base_model) |
| pipe.to("cuda") |
| pipe.load_lora_weights(lora_model_path) |
| |
| save_path = os.path.join(save_folder, "example.jpg") |
| image = pipe(prompt, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale).images[0] |
| image.save(save_path) |
| |
| ``` |
|
|
| ## 📄 Citation |
|
|
| If you find this work useful, please cite us: |
|
|
| ``` |
| @article{zheng2026generative, |
| title={A Generative Foundation Model for Scalable Cytology Image Synthesis in AI-Powered Diagnostics}, |
| author={Zheng, Ke and Zheng, Xueyi and Wang, Jue and Zhang, Xinke and Chen, Shiping and Chen, Qunxi and Fu, Sha and Xie, Dan and Wang, Ruixuan and Lai, Junpeng and others}, |
| journal={Clinical Cancer Research}, |
| pages={OF1--OF12}, |
| year={2026}, |
| publisher={American Association for Cancer Research} |
| } |
| ``` |
|
|
