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| """ | |
| Performance Visualization Module for MedAdapt-SAM | |
| Generates simulated graphs for training history, metrics, and prompt sensitivity studies. | |
| """ | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| import os | |
| def save_plot(plt, filename): | |
| os.makedirs('visualizations', exist_ok=True) | |
| path = os.path.join('visualizations', filename) | |
| plt.savefig(path, bbox_inches='tight', dpi=300) | |
| plt.close() | |
| print(f"Saved plot to {path}") | |
| def plot_training_history(): | |
| """Generates a plot showing Dice score and Loss over epochs""" | |
| epochs = np.arange(1, 101) | |
| # Simulated metrics (converging nicely) | |
| train_loss = 0.8 * np.exp(-epochs/20) + 0.1 | |
| val_loss = 0.85 * np.exp(-epochs/22) + 0.12 + 0.05 * np.random.rand(100) | |
| train_dice = 0.9 * (1 - np.exp(-epochs/15)) | |
| val_dice = 0.88 * (1 - np.exp(-epochs/18)) - 0.03 * np.random.rand(100) | |
| plt.figure(figsize=(12, 5)) | |
| # Loss Plot | |
| plt.subplot(1, 2, 1) | |
| plt.plot(epochs, train_loss, label='Train Loss', color='#1f77b4', linewidth=2) | |
| plt.plot(epochs, val_loss, label='Val Loss', color='#ff7f0e', linestyle='--') | |
| plt.title('Training & Validation Loss', fontsize=14) | |
| plt.xlabel('Epochs') | |
| plt.ylabel('Loss') | |
| plt.grid(True, alpha=0.3) | |
| plt.legend() | |
| # Dice Plot | |
| plt.subplot(1, 2, 2) | |
| plt.plot(epochs, train_dice, label='Train Dice', color='#2ca02c', linewidth=2) | |
| plt.plot(epochs, val_dice, label='Val Dice', color='#d62728', linestyle='--') | |
| plt.axhline(y=0.9, color='gray', linestyle=':', label='Target Threshold') | |
| plt.title('Dice Coefficient History', fontsize=14) | |
| plt.xlabel('Epochs') | |
| plt.ylabel('Dice Score') | |
| plt.grid(True, alpha=0.3) | |
| plt.legend() | |
| plt.tight_layout() | |
| save_plot(plt, 'training_history.png') | |
| def plot_prompt_sensitivity(): | |
| """Generates a bar chart comparing different prompt strategies (Phase 3)""" | |
| strategies = ['No Prompt', '1 Point', '5 Points', 'Box Only', 'Box + 5 Points (Hybrid)'] | |
| dice_scores = [0.45, 0.72, 0.81, 0.86, 0.91] | |
| errors = [0.05, 0.04, 0.03, 0.02, 0.01] | |
| plt.figure(figsize=(10, 6)) | |
| bars = plt.bar(strategies, dice_scores, yerr=errors, capsize=10, color=['#8c564b', '#9467bd', '#bcbd22', '#17becf', '#e377c2']) | |
| plt.title('Phase 3: Prompt Sensitivity Study', fontsize=16) | |
| plt.ylabel('Mean Dice Score', fontsize=12) | |
| plt.ylim(0, 1.0) | |
| plt.grid(axis='y', alpha=0.3) | |
| # Add labels on top of bars | |
| for bar in bars: | |
| yval = bar.get_height() | |
| plt.text(bar.get_x() + bar.get_width()/2, yval + 0.02, f'{yval:.2f}', ha='center', fontweight='bold') | |
| save_plot(plt, 'prompt_sensitivity.png') | |
| def plot_uncertainty_refinement(): | |
| """Generates a plot showing improvement via iterative refinement (Phase 5)""" | |
| iterations = np.arange(0, 6) | |
| dice_unet = [0.82] * 6 | |
| dice_sam_refinement = [0.85, 0.88, 0.90, 0.92, 0.93, 0.93] | |
| plt.figure(figsize=(10, 6)) | |
| plt.plot(iterations, dice_sam_refinement, marker='o', linewidth=3, markersize=8, label='SAM + Uncertainty Refinement', color='#1f77b4') | |
| plt.plot(iterations, dice_unet, linestyle='--', color='gray', label='U-Net Baseline (Static)') | |
| plt.title('Phase 5: Uncertainty-Guided Iterative Refinement', fontsize=16) | |
| plt.xlabel('Refinement Iterations (N)', fontsize=12) | |
| plt.ylabel('Mean Dice Score', fontsize=12) | |
| plt.xticks(iterations) | |
| plt.grid(True, alpha=0.3) | |
| plt.legend(loc='lower right') | |
| save_plot(plt, 'uncertainty_refinement.png') | |
| def plot_model_comparison(): | |
| """Generates a box plot comparing all models (Phase 10)""" | |
| data = [ | |
| np.random.normal(0.82, 0.05, 100), # U-Net | |
| np.random.normal(0.78, 0.08, 100), # Vanilla SAM | |
| np.random.normal(0.86, 0.04, 100), # SAM Adapter | |
| np.random.normal(0.91, 0.02, 100), # MedAdapt-SAM (Final) | |
| ] | |
| labels = ['U-Net', 'Vanilla SAM', 'SAM Adapter', 'MedAdapt-SAM'] | |
| plt.figure(figsize=(10, 6)) | |
| box = plt.boxplot(data, labels=labels, patch_artist=True) | |
| colors = ['#aec7e8', '#ffbb78', '#98df8a', '#ff9896'] | |
| for patch, color in zip(box['boxes'], colors): | |
| patch.set_facecolor(color) | |
| plt.title('Phase 10: Final Model Performance Comparison', fontsize=16) | |
| plt.ylabel('Dice Score distributions', fontsize=12) | |
| plt.grid(axis='y', alpha=0.3) | |
| save_plot(plt, 'final_model_comparison.png') | |
| if __name__ == "__main__": | |
| plot_training_history() | |
| plot_prompt_sensitivity() | |
| plot_uncertainty_refinement() | |
| plot_model_comparison() | |