2.4.0

• Added support for regression (MAE loss) and Cox-based survival analysis for tile-, slide-, and patient-level tasks.
• Added evaluation metrics:
  • Regression: R², Pearson r, Pearson p, MAE, RMSE
  • Survival: Kaplan–Meier curve (optional: low/high risk using a median cut-off computed from train predictions), log-rank p-value, C-index
• Added probability heatmaps for regression and survival tasks.
• Training now supports Attention (ViT), TransMIL, and MLP backbones.
• All backbones support tile-level modeling; slide/patient-level modeling is supported via MLP
• Automatic model class selection based on feature type and task.