Cross-Scanner Robustness Eval

• Cross-scanner robustness evaluations quantify the impact of hardware-induced domain shifts on predictive models using metrics like Dice and CoV.
• They utilize paired and multi-scanner datasets with rigorous validation protocols to ensure reliability across diverse imaging devices.
• Architectural strategies such as transfer learning, latent disentanglement, and modality-specific adaptations enhance model resilience and clinical viability.

Cross-scanner robustness evaluations constitute a core methodology in contemporary biomedical imaging and computational pathology, aiming to quantify and mitigate the effect of hardware-induced domain shifts (scanner bias) on predictive models. These evaluations are essential for guaranteeing that model outputs remain stable, reliable, and clinically useful when deployed across diverse devices, vendors, or acquisition protocols. This article synthesizes state-of-the-art approaches, representative datasets, metrics, and mitigation techniques for cross-scanner robustness, drawing on recent advances in histopathology, magnetic resonance imaging (MRI), positron emission tomography (PET), and computed tomography (CT) as documented in primary literature.

1. Scanner-Induced Domain Shift: Problem Statement and Impact

Scanner-induced domain shift refers to the empirically observed degradation in model performance when input images are acquired on hardware not seen during model training. In digital pathology, whole-slide images (WSIs) captured by different scanners systematically differ in color balance, contrast, sharpness, compression artifacts, and background rendering, even for the same specimen. Similarly, in MRI and PET, variations in magnetic field strength, gradient coils, reconstruction algorithms, and protocol parameters create substantial non-biological heterogeneity across datasets.

The practical consequences include reduced segmentation accuracy, unreliable biomarker extraction, and attenuated generalizability of diagnostic or prognostic models. Foundational studies have shown cross-domain drops in segmentation metrics (Dice, IoU) of up to 0.38 in histopathology, with notable increases in coefficient of variation (CoV) or inter-scanner classification error in both pathology and neuroimaging (Wilm et al., 2023, Carloni et al., 29 Jul 2025).

Maintaining robust performance across scanners is therefore a prerequisite for clinical viability, multi-site studies, and large-scale computational analyses.

2. Benchmark Datasets and Experimental Protocols

Robust evaluation of cross-scanner generalization necessitates datasets that systematically isolate scanner effects, either via scanner-paired design or multi-scanner population sampling.

Scanner-Paired Datasets

• SCORPION: 480 tissue regions, each imaged on five scanners (Leica Aperio AT2, AT450, Ventana DP200, 3DHistech P1000, Philips UFS B300), yields perfectly spatially registered sets for direct prediction consistency measurement (Ryu et al., 28 Jul 2025).
• Canine Cutaneous SCC: 44 tumors × 5 scanners (Leica, Hamamatsu, 3DHistech), with pixel-wise polygon registration to align ground truth (Wilm et al., 2023).

Multi-Scanner Population Datasets

• COSAS Challenge: Images segmented from WSIs acquired on up to six different scanners, with separated training/validation/test splits (Qayyum et al., 23 Sep 2024, Kim et al., 20 Sep 2024, Cai et al., 18 Sep 2024, Galdran, 20 Sep 2024, Jiayan et al., 19 Sep 2024).
• ADNI, IXI, PPMI (Neuroimaging): T1-weighted MRI and PET volumes across 5–10 scanners from multiple vendors (Caldera et al., 6 May 2025, Carloni et al., 29 Jul 2025, Caldera et al., 24 Oct 2025, Ades-Aron et al., 8 Jul 2024).

Common protocol: models are trained exclusively on data from a subset of scanners and evaluated on held-out hardware to assess scanner-induced generalization gaps. Cross-validation folds are often constructed so that each scanner or participating center is withheld in turn for unbiased estimation of domain shift (Jiayan et al., 19 Sep 2024, Galdran, 20 Sep 2024).

3. Quantitative Evaluation Metrics and Reporting

A characteristic of state-of-the-art cross-scanner robustness evaluations is the use of task-specific performance metrics decomposed by scanner domain, as well as direct measures of prediction consistency.

Segmentation and Classification Metrics

• Dice Coefficient: $\mathrm{Dice}(P,G) = \frac{2 |P\cap G|}{|P| + |G|}$
• Intersection over Union (IoU/Jaccard): $\mathrm{IoU}(P,G) = \frac{|P\cap G|}{|P\cup G|}$
• Coefficient of Variation (CoV): For model prediction $\hat{y}_m^s$ across scanners $s$ for specimen $m$, $$\mathrm{CoV} = \frac{\sigma(\hat{y}_m^{s_1},\dots,\hat{y}_m^{s_S})}{|\mu(\hat{y}_m^{s_1},\dots,\hat{y}_m^{s_S})|}$$ (Carloni et al., 29 Jul 2025).

Scanner Consistency Metrics

For paired datasets, Editor’s term: “Scanner-Paired Dice” between output masks from corresponding tissues imaged on different scanners provides a direct quantification of scanner-induced prediction disagreement:

$$\mathrm{Dice}_{k}^{(i,j)} = \frac{2 |F(x_k^{(i)}) \cap F(x_k^{(j)})|}{|F(x_k^{(i)})| + |F(x_k^{(j)})|}$$

Aggregated as mean (AvgConsistency) or minimum (MinConsistency) across scanner pairs (Ryu et al., 28 Jul 2025).

Statistical Reporting

Aggregate performance (mean/stdev), per-scanner scores, robustness gaps ($\Delta_{\text{scanner}}$), and, where available, statistical significance by bootstrap or paired t-tests are reported. However, many challenge submissions and applied works report only overall or cross-validation scores without per-scanner breakdowns (Qayyum et al., 23 Sep 2024, Kim et al., 20 Sep 2024).

4. Architectural and Algorithmic Strategies for Robustness

Transfer Learning and Pretraining

Pre-initialization of model backbones on large, diverse datasets (e.g., ImageNet, vision foundation models) provides strong baseline generalization to unseen scanner domains due to broad encoded color/texture invariance (Qayyum et al., 23 Sep 2024, Cai et al., 18 Sep 2024).

Domain Stratification and Ensembling

Explicitly stratifying training by scanner (“domain-stratified training”), followed by model ensembling (hard voting or probability averaging) across scanner-specific models, reduces prediction variance and balances residual scanner biases. This is evidenced by cross-validation DSC increases and reduction in inter-scanner gaps (Jiayan et al., 19 Sep 2024).

Latent Disentanglement and Harmonization

Latent-space disentanglement separates anatomical and scanner codes, enabling harmonization via targeted style transfer or scanner-free mapping. Notable approaches:

• DISARM++: Enforces scanner-free invariance in T1 MRI via encoder-generator adversarial training and scanner-invariant losses, achieving state-of-the-art reduction in histogram divergence and inter-scanner error (Caldera et al., 6 May 2025).
• SSIM-Guided Harmonization: Networks trained with differentiable SSIM-based loss preserve anatomy while minimizing contrast/luminance variability, resulting in harmonized images with structural SSIM $>0.97$ across 6+ test scanners (Caldera et al., 24 Oct 2025).

Consistency Loss and Contrastive Learning

• SimCons: Supervises output similarity between style-augmented and original images during training; combined with color/stain/Fourier augmentations, this yields 1.5–2% increases in AvgConsistency and MinConsistency rates on SCORPION, with simultaneous Dice improvement (Ryu et al., 28 Jul 2025).
• ScanGen: Contrastive loss on foundation model embeddings of scanner-paired images (attract same-tissue/different-scanner, repel different-tissue/same-scanner), reducing output CoV by up to 70% with no loss in classification AUC (Carloni et al., 29 Jul 2025).

Model-Specific Adaptations

• Parameter-Efficient Fine Tuning (PEFT): Mix-PEFT (LoRA, VPT, SSF)-based adaptation schemes for PET and MRI allow rapid fine-tuning to a new scanner using $<1\%$ of parameters, matching or exceeding task metrics of full model retraining (Kim et al., 10 Jul 2024).

Denoising and Harmonization Pipelines

For MRI, complex-valued MPPCA denoising, phase unwinding, and subsequent harmonization with linear-RISH substantially reduce cross-scanner coefficient of variation, improve intraclass correlation coefficient (ICC), and halve required sample sizes for effect detection in diffusion biomarker studies (Ades-Aron et al., 8 Jul 2024).

5. Failure Modes, Limitations, and Recommendations

Observed scanner-induced performance degradation often arises from:

• Network overfitting to scanner-specific cues (e.g., background intensity, color contrast) instead of relevant pathology (Wilm et al., 2023).
• Unmitigated shifts in stain, sharpness, or hardware-specific preprocessing (Ryu et al., 28 Jul 2025).
• Inadequate augmentation schemes or harmonization pipelines.

Future improvements are expected by:

• Systematic integration of domain-adversarial modules, explicit stain/contrast normalization, and meta-learning techniques.
• Expanding scanner diversity and stain protocols in calibration datasets.
• Conducting rigorous ablations to isolate the effects of attention, disentanglement, or token-based adaptation mechanisms.

Ensemble approaches and domain-stratified validation remain standard best practices; however, over-regularization (excessive consistency enforcement) can cause mode collapse in predictions (Ryu et al., 28 Jul 2025).

6. Practical Guidelines and Benchmarking Standards

State-of-the-art practice in cross-scanner robustness evaluations comprises:

Element	Standard Practice	Reference
Dataset Design	Scanner-paired spatial registration or population-based scanner splits	(Ryu et al., 28 Jul 2025, Wilm et al., 2023)
Metric Choice	Task accuracy (Dice, AUC) & scanner consistency (paired Dice, CoV)	(Carloni et al., 29 Jul 2025, Ryu et al., 28 Jul 2025)
Training Strategy	Pretrained backbone, scanner/domain stratification, model ensemble or harmonization	(Jiayan et al., 19 Sep 2024, Caldera et al., 6 May 2025)
Robustness Mitigation	Augmentation + consistency loss (SimCons), contrastive loss (ScanGen), stain normalization	(Ryu et al., 28 Jul 2025, Carloni et al., 29 Jul 2025, Wilm et al., 2023)
Quantitative Reporting	In-domain vs. cross-domain split, cross-validation, per-scanner analysis where possible	(Jiayan et al., 19 Sep 2024, Cai et al., 18 Sep 2024)

A critical guideline is the inclusion of at least one scanner-paired dataset in the test suite; without it, estimation of worst-case prediction discrepancy is impossible (Ryu et al., 28 Jul 2025). Non-paired test data should always be split along scanner boundaries, and statistical uncertainty (via cross-validation or bootstrap) should be reported.

Scanner harmonization models—particularly those enforcing domain-agnostic latent coding (e.g., DISARM++, SSIM-guided architectures)—are recommended for applications where anatomical fidelity and quantification are essential. Conversely, for classification or MIL tasks, contrastive alignment directly on output logits or features is more appropriate (Carloni et al., 29 Jul 2025).

7. Outlook and Future Directions

While foundational models with extensive pretraining and token-based adaptation (e.g., Rein, Mix-PEFT) significantly narrow cross-scanner generalization gaps, the field recognizes residual challenges:

• Deployment in low-data or rare-scanner scenarios may necessitate few-shot or lifelong learning protocols.
• Non-H&E stains, rare pathologies, and emerging acquisition devices remain incompletely covered by current benchmarks.
• Clinical integration will require federated, black-box validation frameworks with physics-inspired test-time augmentation strategies as exemplified in CT (Highton et al., 27 Jun 2024).

As the volume and diversity of multi-scanner datasets increase, and harmonization/consistency objectives become more sophisticated, cross-scanner robustness evaluation will remain central to trustworthy computational medicine, with robust, standardized protocols enabling reliable deployment in multi-institutional settings.