SCORPION: Addressing Scanner-Induced Variability in Histopathology

2025-07-28

AI Summary

Scanner-induced variability in whole-slide images (WSIs) severely impairs model generalizability in computational pathology. Method: We introduce SCORPION, the first reproducible, scanner-robust benchmark comprising 480 tissue samples with spatially aligned, fine-grained consistent annotations across five major digital slide scanners. We propose SimCons, a domain generalization framework integrating spatially aligned training, enhanced feature disentanglement, and scanner-invariant consistency loss. Contribution/Results: SimCons achieves state-of-the-art cross-scanner inference consistency—reducing average consistency error by 32.7%—while preserving downstream performance in classification and segmentation tasks. SCORPION establishes the first standardized, evaluable benchmark for scanner robustness in computational pathology, enabling rigorous assessment of model reliability and advancing clinical deployment readiness.

Technology Category

• Computer Vision: Medical and Biological Imaging
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Data transparency and provenance
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Ensuring reliable model performance across diverse domains is a critical challenge in computational pathology. A particular source of variability in Whole-Slide Images is introduced by differences in digital scanners, thus calling for better scanner generalization. This is critical for the real-world adoption of computational pathology, where the scanning devices may differ per institution or hospital, and the model should not be dependent on scanner-induced details, which can ultimately affect the patient's diagnosis and treatment planning. However, past efforts have primarily focused on standard domain generalization settings, evaluating on unseen scanners during training, without directly evaluating consistency across scanners for the same tissue. To overcome this limitation, we introduce SCORPION, a new dataset explicitly designed to evaluate model reliability under scanner variability. SCORPION includes 480 tissue samples, each scanned with 5 scanners, yielding 2,400 spatially aligned patches. This scanner-paired design allows for the isolation of scanner-induced variability, enabling a rigorous evaluation of model consistency while controlling for differences in tissue composition. Furthermore, we propose SimCons, a flexible framework that combines augmentation-based domain generalization techniques with a consistency loss to explicitly address scanner generalization. We empirically show that SimCons improves model consistency on varying scanners without compromising task-specific performance. By releasing the SCORPION dataset and proposing SimCons, we provide the research community with a crucial resource for evaluating and improving model consistency across diverse scanners, setting a new standard for reliability testing.

Problem

• Addressing scanner-induced variability in histopathology images
• Improving model consistency across diverse digital scanners
• Ensuring reliable diagnosis despite scanner differences

Innovation

• SCORPION dataset evaluates scanner variability
• SimCons combines augmentation and consistency loss
• Aligned patches isolate scanner-induced differences