| --- |
| license: apache-2.0 |
| pipeline_tag: text-to-image |
| tags: |
| - medical |
| - retinal-imaging |
| - fundus |
| - ophthalmology |
| - text-to-image |
| - diffusion |
| library_name: pytorch |
|
|
| extra_gated_heading: "Access to RetinaLogos Model Weights" |
| extra_gated_description: >- |
| RetinaLogos is an open model released under Apache-2.0. To help us understand |
| usage and stay in touch with the community, please fill in the short form |
| below before downloading. Access is granted automatically. |
| extra_gated_prompt: >- |
| By requesting access, you confirm that the information provided is accurate |
| and agree to the terms below. Generated images are synthetic and must not be |
| used for clinical diagnosis. |
| extra_gated_fields: |
| Name: text |
| Affiliation: text |
| Country: country |
| Intended use: |
| type: select |
| options: |
| - Academic Research |
| - Educational Use |
| - Commercial Use |
| - Other |
| I will cite the RetinaLogos MICCAI 2025 paper in any work using this model: checkbox |
| I understand generated images are synthetic and will NOT use them for clinical diagnosis or patient care: checkbox |
| I accept the terms of the Apache License version 2: checkbox |
| extra_gated_button_content: "Agree and Access Model" |
| --- |
| |
| # RetinaLogos: Fine-Grained Synthesis of High-Resolution Retinal Images Through Captions |
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| [](https://papers.miccai.org/miccai-2025/paper/0673_paper.pdf) |
| [](https://github.com/uni-medical/retina-text2cfp) |
|
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| **RetinaLogos** is a text-to-image framework that generates high-resolution Color Fundus Photographs (CFPs) from free-form textual descriptions of retinal conditions. Trained on **RetinaLogos-1400k**, a synthetic dataset of 1.4 million retinal caption–image pairs derived from Electronic Health Records (EHRs), it provides fine-grained semantic control over retinal anatomical structures, lesion types, and disease progression. |
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| Published at **MICCAI 2025** (LNCS 15975, pp. 474–484). |
|
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| ## Model Highlights |
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| - **Fine-grained control**: precise control over retinal anatomy and disease stages via free-form text prompts. |
| - **Clinically plausible**: 62.07% of text-driven synthetic images are indistinguishable from real CFPs by ophthalmologists. |
| - **Downstream gains**: 10%–25% improvement in diabetic retinopathy grading and glaucoma detection when used for augmentation. |
| - **Architecture**: built upon the [Lumina-Next](https://github.com/Alpha-VLLM/Lumina-T2X/tree/main/lumina_next_t2i) text-to-image framework (Apache 2.0). |
|
|
| ## Repository Contents |
|
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| This repository hosts the **stage-3 training checkpoint**: |
|
|
| | File | Size | Description | |
| |------|------|-------------| |
| | `stage3_checkpoints7/consolidated.00-of-01.pth` | ~6.1 GB | Consolidated model weights | |
| | `stage3_checkpoints7/model_args.pth` | 1.5 KB | Model architecture arguments | |
|
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| ## Usage |
|
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| Inference and training code, environment setup, and detailed instructions are in the GitHub repository: **[uni-medical/retina-text2cfp](https://github.com/uni-medical/retina-text2cfp)** (see `codes/README.md`). Download this checkpoint, point the inference script's model path to `stage3_checkpoints7/`, and follow the instructions there. |
|
|
| ```python |
| from huggingface_hub import snapshot_download |
| |
| ckpt_dir = snapshot_download(repo_id="junzhin/RetinaLogo") |
| ``` |
|
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| ## Intended Use & Limitations |
|
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| This model is intended for **research purposes** — data augmentation, ophthalmology education, and the study of retinal disease representation. Generated images are synthetic and must **not** be used for clinical diagnosis or as a substitute for real patient imaging. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @InProceedings{NinJun_RetinaLogos_MICCAI2025, |
| author = { Ning, Junzhi and Tang, Cheng and Zhou, Kaijing and Song, Diping and Liu, Lihao and Hu, Ming and Li, Wei and Xu, Huihui and Su, Yanzhou and Li, Tianbin and Liu, Jiyao and Ye, Jin and Zhang, Sheng and Ji, Yuanfeng and He, Junjun}, |
| title = { { RetinaLogos: Fine-Grained Synthesis of High-Resolution Retinal Images Through Captions } }, |
| booktitle = {proceedings of Medical Image Computing and Computer Assisted Intervention -- MICCAI 2025}, |
| year = {2025}, |
| publisher = {Springer Nature Switzerland}, |
| volume = {LNCS 15975}, |
| month = {September}, |
| page = {474 -- 484} |
| } |
| ``` |
|
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| ## Acknowledgments |
|
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| We thank the authors of [Lumina-Next](https://github.com/Alpha-VLLM/Lumina-T2X/tree/main/lumina_next_t2i) and [Lumina 2.0](https://github.com/Alpha-VLLM/Lumina-Image-2.0) for their open-source frameworks. |
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