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.

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']}")

halituyanik/RetinaGen-VLM

👁️ RetinaGen-VLM

Project Overview

Key Features

Methodology

Clinical Reasoning Chain

Implementation Preview

halituyanik/
RetinaGen-VLM