Whole Slide Image Quality Control

Problem: Whole slide images always come with some processing artifacts: tissue folds, marker lines, uneven sectioning, bubbles, out-of-focus regions, etc. However, machine learning models typically don’t handle these detects in a robust way – especially if they were excluded from the training data. Therefore, image quality control is essential.

Solution: This project involved researching and reviewing published articles on detecting a variety of artifacts, followed by formulating a plan for detecting the most common defects. Some can be detected by fairly simplistic algorithms, but deep learning is usually the most accurate – given sufficient training data. Some artifacts can be simulated to create additional training data, while others require extensive annotation efforts.