Abstract
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.
Abstract (translated)
泛化是计算病理学中的主要挑战之一。Slide preparation的不一致性和扫描仪的多样性在使用训练期间未曾看到的医疗中心数据时会导致模型性能不佳。为了在乳腺癌 patch 分类中实现染色不变异,我们采用了无监督图像到图像转换的循环GAN策略。我们比较了三种循环GAN基 approach 与在没有染色不变异策略的情况下获得的基准分类模型。提议的两种 approach 使用循环GAN的推断或训练 translation 来构建特定的染色分类模型。最后一种方法在训练期间使用它们来增加目标染色的数据。这限制了分类模型学习染色不变异特征。基准指标是通过训练和测试基准分类模型在参考染色上进行设置。我们使用三个医疗中心 H&E 和 H&E&S 染色评估了表现。在所有方法中,该研究提高了基准指标,无需对目标染色标签。基于染色增加的方法在每个染色上取得了最佳结果。每种方法的优点和缺点在本文中研究和讨论。然而,训练高性能的循环GAN模型本身 represents a challenge。在本研究中,我们介绍了一种系统的方法,通过设置一种新的停止准则来优化循环GAN训练。这种方法无需对循环GAN结果进行视觉检查,并证明了使用事先定义的训练 epochs 的方法的优越性。此外,我们还研究了循环GAN训练所需的最小数据量。
URL
https://arxiv.org/abs/2301.13128
