SCORPION：解决组织病理学中由扫描仪引起的变异性
SCORPION: Addressing Scanner-Induced Variability in Histopathology
AI总结：
针对计算病理学中不同扫描仪导致的模型性能变异问题，本文提出SCORPION数据集（480个组织样本、5台扫描仪、2400个对齐图像块）和SimCons框架，通过一致性损失结合增强泛化技术提升跨扫描仪模型一致性，且不牺牲任务性能。
摘要：
确保模型在不同领域中的可靠性能是计算病理学中的一项关键挑战。全切片图像中一个特定的变异性来源是由不同数字扫描仪之间的差异所引入的，因此需要更好的扫描仪泛化能力。这对于计算病理学的实际应用至关重要，因为不同机构或医院的扫描设备可能不同，模型不应依赖于扫描仪引起的细节，这些细节最终可能影响患者的诊断和治疗计划。然而，以往的工作主要集中于标准的领域泛化设置，在训练期间评估未见过的扫描仪，而没有直接评估同一组织在不同扫描仪之间的一致性。为了克服这一限制，我们引入了SCORPION，这是一个专门设计用于评估扫描仪变异性下模型可靠性的新数据集。SCORPION包含480个组织样本，每个样本使用5台扫描仪进行扫描，产生2,400个空间对齐的图像块。这种扫描仪配对设计允许隔离扫描仪引起的变异性，从而在控制组织组成差异的同时，对模型一致性进行严格评估。此外，我们提出了SimCons，这是一个灵活的框架，将基于增强的领域泛化技术与一致性损失相结合，以明确解决扫描仪泛化问题。我们通过实验证明，SimCons在不牺牲任务特定性能的情况下，提高了模型在不同扫描仪上的一致性。通过发布SCORPION数据集并提出SimCons，我们为研究社区提供了一个关键资源，用于评估和改进跨不同扫描仪的模型一致性，为可靠性测试设立了新的标准。
英文摘要：
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.
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