Speakers
Description
In this workshop on image prevalidation, we will introduce the importance of ensuring that image data is suitable for the analysis it is intended for. Images can contain hidden quality issues, inconsistencies, or artifacts that can impact analysis results, often in subtle ways. Through real-world examples, we will explore how to detect these challenges early on, discussing image quality and consistency across data collections. We aim to create an open, interactive environment where participants can share their experiences and challenges with image data. We will introduce practical approaches to assessing image data before analysis begins, while encouraging discussion on best practices and common pitfalls. Our goal is to foster communication and mutual learning, helping everyone to improve their image validation workflows.
Any Specific Requirements or Equipment Needed?
Python knowledge is helpful, but not required.
