用于乳腺组织病理学浸润性癌分类中无监督染色适配的标准化CycleGAN训练
Standardized CycleGAN training for unsupervised stain adaptation in invasive carcinoma classification for breast histopathology
AI总结：
为解决乳腺浸润性癌分类中的染色泛化难题，本文提出基于CycleGAN的无监督染色转换与增强策略，并引入一种无需视觉检查的标准化停止准则来优化CycleGAN训练，显著提升了多中心不同染色数据下的分类性能。
摘要：
泛化能力是计算病理学面临的主要挑战之一。切片制备的异质性和扫描仪的多样性导致模型在应用于训练期间未见过的医疗中心数据时性能不佳。为了在乳腺浸润性癌图像块分类中实现染色不变性，我们采用基于cycleGANs的染色转换策略进行无监督图像到图像转换。我们将三种基于cycleGAN的方法与未采用任何染色不变性策略的基线分类模型进行比较。其中两种方法在推理或训练阶段使用cycleGAN的转换结果，以构建特定染色的分类模型。最后一种方法在训练期间将其用于染色数据增强。这迫使分类模型学习染色不变特征。基线指标通过在参考染色上训练和测试基线分类模型来设定。我们使用三个医疗中心的H&E和H&E&S染色数据评估性能。本研究测试的每种方法均无需目标染色标签即可提升基线指标。基于染色增强的方法在所有染色上均取得最佳结果。本文研究并讨论了每种方法的优缺点。然而，训练高性能cycleGAN模型本身也是一项挑战。在本研究中，我们通过设定一种新颖的停止准则，引入了一种优化cycleGAN训练的系统化方法。该方法无需对cycleGAN结果进行任何视觉检查，并证明其优于使用预定义训练轮数的方法。此外，我们还研究了cycleGAN训练所需的最小数据量。
英文摘要：
Generalization is one of the main challenges of computational pathology. Slide preparation heterogeneity and the diversity of scanners lead to poor model performance when used on data from medical centers not seen during training. In order to achieve stain invariance in breast invasive carcinoma patch classification, we implement a stain translation strategy using cycleGANs for unsupervised image-to-image translation. We compare three cycleGAN-based approaches to a baseline classification model obtained without any stain invariance strategy. Two of the proposed approaches use cycleGAN's translations at inference or training in order to build stain-specific classification models. The last method uses them for stain data augmentation during training. This constrains the classification model to learn stain-invariant features. Baseline metrics are set by training and testing the baseline classification model on a reference stain. We assessed performances using three medical centers with H&E and H&E&S staining. Every approach tested in this study improves baseline metrics without needing labels on target stains. The stain augmentation-based approach produced the best results on every stain. Each method's pros and cons are studied and discussed in this paper. However, training highly performing cycleGANs models in itself represents a challenge. In this work, we introduce a systematical method for optimizing cycleGAN training by setting a novel stopping criterion. This method has the benefit of not requiring any visual inspection of cycleGAN results and proves superiority to methods using a predefined number of training epochs. In addition, we also study the minimal amount of data required for cycleGAN training.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
