Update README.md

README.md

@@ -22,19 +22,19 @@ model_name: "RetinaGen-VLM"
 # 👁️ RetinaGen-VLM
 **Vision-Language Alignment for Automated Retinopathy Grading**
 
-### 📝 Project Overview
+###  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
+### 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
+###  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
+####  Clinical Reasoning Chain
 The model simulates clinical logic by identifying specific visual biomarkers before generating the final diagnostic output:
 
 **Process Flow:**
@@ -43,7 +43,7 @@ The model simulates clinical logic by identifying specific visual biomarkers bef
 **Example Output:**
 > "Optic disc shows increased cup-to-disc ratio consistent with glaucoma symptoms."
 
-### 💻 Implementation Preview
+### Implementation Preview
 ```python
 import torch
 from retinagen_vlm import VQVAE, MedicalTransformer