Enhance Training and Inference Flexibility

Overview

This PR introduces several updates aimed at increasing the flexibility of the training and inference processes.

Parallel Fold Training: Updated README.md with instructions for training multiple folds simultaneously, improving the speed and efficiency of model training.
Training Script Flexibility: Modified the training script to accept additional arguments such as GPU device, desired folds, dataset name, and dataset ID, allowing for more customizable training sessions.
Dataset Naming Consistency: Fixed the naming convention in the aggregated dataset to align with the dataset name in the nnunet_scripts/aggregate_data.py file, ensuring consistency and clarity in dataset management.

Added authors to the repository documentation to acknowledge contributions and facilitate collaboration.