Robust sensitivity control in digital pathology via tile score distribution matching
This addresses the problem of reliable deployment of computational pathology systems for clinicians, though it is incremental as it builds on existing domain generalization and MIL methods.
The paper tackles the challenge of controlling sensitivity levels in digital pathology models across medical centers, achieving robust sensitivity control with only a few calibration samples.
Deploying digital pathology models across medical centers is challenging due to distribution shifts. Recent advances in domain generalization improve model transferability in terms of aggregated performance measured by the Area Under Curve (AUC). However, clinical regulations often require to control the transferability of other metrics, such as prescribed sensitivity levels. We introduce a novel approach to control the sensitivity of whole slide image (WSI) classification models, based on optimal transport and Multiple Instance Learning (MIL). Validated across multiple cohorts and tasks, our method enables robust sensitivity control with only a handful of calibration samples, providing a practical solution for reliable deployment of computational pathology systems.