README.md

---
language:
- en
license: apache-2.0
tags:
- medical
- ophthalmology
- vision-language-model
- retinopathy
- healthcare
- vq-vae
- multimodal
datasets:
- EyePACS
- MESSIDOR
metrics:
- accuracy
- f1
model_name: "RetinaGen-VLM"
---

# 👁️ RetinaGen-VLM
**Vision-Language Alignment for Automated Retinopathy Grading**

###  Project Overview
RetinaGen-VLM is a multimodal deep learning framework designed to bridge the gap between fundus imaging and clinical reporting. By leveraging a **VQ-VAE** based discrete latent space and an autoregressive **Transformer**, the model identifies diabetic retinopathy stages while generating descriptive medical narratives.

![RetinaGen-VLM Architecture](architecture.png)

### Key Features
- **Multimodal Reasoning:** Aligns visual features directly with medical terminology.
- **Synthetic Data Augmentation:** Utilizes generative modeling to balance rare pathological cases such as PDR.
- **Automated Grading:** Provides a standardized 5-point scale diagnostic output (Stages 0-4).

###  Methodology
The core architecture focuses on mapping high-resolution fundus images into a quantized codebook (Zq), followed by a Transformer-based decoder that predicts the likelihood of specific clinical biomarkers.
####  Clinical Reasoning Chain
The model simulates clinical logic by identifying specific visual biomarkers before generating the final diagnostic output:

**Process Flow:**
`optic_disc` → `cup_ratio` → `vessel_tortuosity` → `hemorrhage`

**Example Output:**
> "Optic disc shows increased cup-to-disc ratio consistent with glaucoma symptoms."

### Implementation Preview
```python
import torch
from retinagen_vlm import VQVAE, MedicalTransformer

# Loading the pre-trained architecture
model = VQVAE.load_from_checkpoint("retina_v1.ckpt")
vlm_engine = MedicalTransformer(vocab_size=50000)

# Generating clinical narrative from fundus image
z_q, _ = model.encode(fundus_image)
prediction = vlm_engine.generate(z_q)

print(f"Diagnostic Stage: {prediction['stage']}")
print(f"Clinical Narrative: {prediction['report']}")