Bias Assessment and Data Drift Detection in Medical Image Analysis: A Survey
September 2024
in “
arXiv (Cornell University)
”
TLDR Reliable machine learning in medical imaging needs bias checks and data drift detection for consistent performance.
This survey reviews methods to ensure the reliability of machine learning models in medical imaging analysis, focusing on bias assessment and data drift detection. It categorizes techniques for evaluating models' inner workings, particularly in disease classification, and discusses methods for estimating classifier accuracy without ground truth labels. The goal is to enhance the trustworthiness and integration of these models into clinical settings by maintaining consistent prediction performance over time.
