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.

Osama03
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Medical-X-ray-image-generation-stable-diffusion