SCORPION: Addressing Scanner-Induced Variability in Histopathology

Jeongun Ryu, Heon Song, Seungeun Lee, Soo Ick Cho, Jiwon Shin, Kyunghyun Paeng, Sérgio Pereira

Keywords: Computational Pathology · Scanner Generalization.

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
dataset1 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.

Submission Number: 10