```python
import torch
from monai.networks.nets import UNet
from torch.optim import SGD

class SupervisedMonaiProstateLearner:
    # This is a placeholder for the base class
    pass

class SupervisedMonaiProstateDittoLearner(SupervisedMonaiProstateLearner):
    def __init__(self, config_filename, num_epochs_global, num_epochs_personal, task_name):
        self.config_filename = config_filename
        self.num_epochs_global = num_epochs_global
        self.num_epochs_personal = num_epochs_personal
        self.task_name = task_name

    def configure_training(self):
        # Initialize the SupervisedPTDittoHelper with UNet model and SGD optimizer
        self.model = UNet(
            dimensions=3,
            in_channels=1,
            out_channels=1,
            channels=(16, 32, 64, 128, 256),
            strides=(2, 2, 2, 2),
            num_res_units=2,
        )
        self.optimizer = SGD(self.model.parameters(), lr=0.01, momentum=0.9)
        # Additional configuration can be added here

    def train(self):
        try:
            # Example pipeline for Ditto training
            # 1. Get global model weights
            # This is a placeholder step
            global_weights = None

            # 2. Prepare for fedprox loss
            # Placeholder for fedprox loss preparation

            # 3. Load Ditto personalized model info
            # Placeholder for loading Ditto personalized model info

            # 4. Local training of the reference model
            for epoch in range(self.num_epochs_global):
                # Placeholder for training logic
                pass

            # 5. Local training of the personalized model
            for epoch in range(self.num_epochs_personal):
                # Placeholder for training logic
                pass

            # 6. Return updated weights of the reference model
            updated_weights = self.model.state_dict()  # Example of returning the model's state dict
            return updated_weights

        except KeyboardInterrupt:
            # Handle abort signals
            print("Training aborted.")
        except Exception as e:
            # Handle other exceptions
            print(f"An error occurred during training: {e}")
```