--- 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']}")