The success of digital pathology and image analysis depends on more than powerful software. Reliable results require robust algorithm development, risk-based validation, comprehensive quality control, and strong data integrity practices throughout the workflow. This session will present practical approaches for developing, testing, and maintaining image analysis algorithms, with a focus on managing variability in tissue preparation, staining, scanning, annotation, and analysis. Attendees will learn strategies for optimizing classifier performance, implementing effective QC checkpoints, identifying sources of analytical error, and ensuring reproducible, high-quality results. Real-world examples will highlight how structured validation and quality management practices can improve confidence in quantitative pathology data across research and translational applications.

Learning Objectives

1. Describe best practices for digital pathology algorithm development and validation.
2. Identify quality control measures that improve accuracy, reproducibility, and data integrity.
3. Recognize common sources of analytical error and strategies for improving algorithm robustness.
4. Apply practical approaches to maintaining reliable digital pathology workflows.