Medical X ray image generation stable diffusion
by Osama03
Open source · 17k downloads · 6 likes
1.1
(6 reviews)ImageAPI & LocalAbout
This AI model is a specialized medical image generator designed to convert textual descriptions of symptoms into realistic medical scans, such as X-rays, CT scans, or MRIs. It is based on a modified version of Stable Diffusion, fine-tuned using the LoRA method to produce plausible medical images while maintaining high visual quality and anatomical consistency. Primarily intended for educational or research purposes, it enables the visualization of potential diagnostic scenarios based on symptoms described in natural language, without claiming to replace professional medical advice. What sets it apart is its ability to generate varied and realistic medical images from text prompts while remaining accessible and efficient through a lightweight adaptation of the base model. However, its use is strictly regulated to prevent any application in real clinical or diagnostic contexts.
Documentation
Symptom-to-Medical-Image Generator
This project introduces a text-to-image diffusion model fine-tuned using LoRA (Low-Rank Adaptation) on top of CompVis/stable-diffusion-v1-4 for the task of medical image generation. The model generates X-ray, CT, or MRI scans based on natural language descriptions of patient symptoms, offering a novel way to visualize potential diagnostic outcomes.
What Is This Model?
This is a domain-adapted diffusion model tailored to generate realistic medical scans conditioned on symptom prompts. The model was fine-tuned using LoRA, which allowed for:
- Efficient training without modifying the original model weights.
- Adaptation to a smaller, highly-specialized medical dataset.
- Retention of high-quality generative capabilities from the base model.
Key Features
- Symptom-to-scan generation: Input symptoms in plain English and receive a plausible X-ray, CT, or MRI image.
- Multi-modality support: Generate different types of scans (e.g., chest X-rays, brain MRIs) depending on the prompt context.
- High realism: Outputs are visually realistic and follow anatomical structure, trained using real medical datasets.
When Can You Use This Model?
Use Cases
| Application Area | Description |
|---|---|
| Medical Research | Generate datasets for hypothesis testing or model training without using real patient data. |
| Education & Training | Teach students about correlations between symptoms and imaging in an interactive way. |
| AI-Aided Prototyping | Test downstream diagnostic pipelines on synthetic but realistic image data. |
| Data Augmentation | Enrich datasets for training classification/segmentation models. |
| Prompt-Based Exploration | Investigate how changes in symptoms affect image generation (e.g., how “fever + cough” differs from “chest pain + shortness of breath”). |
Not for Use In:
- Real-world clinical diagnosis or decision-making
- Generating scans for real patients or influencing treatment
- Bypassing ethical or regulatory controls in medical AI
Example Usage
Input Prompt:
"I've been feeling really out of breath lately, especially when I'm walking up a flight of stairs or doing some light exercise. It's like my chest gets tight and I can't catch my breath. "
Output:
The model generates a chest X-ray image that corresponds to symptoms of a potential pulmonary issue.
Under the Hood
- Base Model:
CompVis/stable-diffusion-v1-4 - Fine-tuning Method: LoRA (efficient, parameter-light adaptation)
- Dataset: Custom dataset of symptom-to-image pairs, curated for medical imaging consistency
- Framework: PyTorch + 🤗 Diffusers + Hugging Face Spaces
Ethical & Legal Disclaimer
This model is strictly intended for research and educational use. It is not a substitute for professional medical judgment. Use of synthetic medical images should follow all local regulatory and ethical guidelines.
Capabilities & Tags
diffuserssafetensorsmedicalX-rayDiffusionGenerationText-to-imagestable-diffusionlorafine_tune
Links & Resources