A Knowledge-Based Method for Detecting Network-Induced Shape Artifacts in Synthetic Images

Abstract

The adoption of synthetic medical images for training or testing without thorough quality assessment risks introducing artifacts and unrealistic features that can mislead machine learning models and compromise clinical utility. This work introduces a novel knowledge-based method for detecting network-induced shape artifacts in synthetic images. The method can identify anatomically unrealistic images, detect shape artifacts irrespective of the generative model, and offer interpretability through its knowledge-driven design. We validated the method using two synthetic mammography datasets and demonstrated its effectiveness in flagging images with network-induced artifacts. A reader study further confirmed these findings and showed that the most anomalous images identified by the method were also flagged by human readers. This method provides a step toward the responsible use of synthetic data by ensuring synthetic images align with realistic morphological and anatomical constraints.