You are working as a data engineer for a healthcare technology startup that specializes in developing machine learning models to assist medical professionals in diagnosing diseases from medical imaging data. Your team is responsible for designing and implementing data pipelines to pre$-$process label, augment and validate the imaging datasets used to train and evaluate these models. Imagine you have been tasked with designing a comprehensive data engineering and ML engineering pipeline for the healthcare startup's medical imaging project.

$($i$)$ How would you approach the data labelling process for medical imaging datasets, considering the critical importance of accurate annotations for training machine learning models?

$($ii$)$ Outline the process of data validation, including techniques for detecting and handling data anomalies, outliers and inconsistencies in medical imaging datasets.