StyPath: Style-Transfer Data Augmentation For Robust Histology Image Classificat

StyPath: Style-Transfer Data Augmentation For Robust Histology Image Classification

The classification of Antibody Mediated Rejection (AMR) in kidney transplant remains challenging even for experienced nephropathologists; this is partly because histological tissue stain analysis is often characterized by low inter-observer agreement and poor reproducibility. One of the implicated causes for inter-observer disagreement is the variability of tissue stain quality between (and within) pathology labs, coupled with the gradual fading of archival sections.Variations in stain colors and intensities can make tissue evaluation difficult for pathologists, ultimately affecting their ability to describe relevant morphological features. Being able to accurately predict the AMR status based on kidney histology images is crucial for improving patient treatment and care. We propose a novel pipeline to build robust deep neural networks for AMR classification based on StyPath, a histological data augmentation technique that leverages a light weight style-transfer algorithm as a means to reduce sample-specific bias. Each image was generated in 1.84 +- 0.03 seconds using a single GTX TITAN V gpu and pytorch, making it faster than other popular histological data augmentation techniques. We evaluated our model using a Monte Carlo (MC) estimate of Bayesian performance and generate an epistemic measure of uncertainty to compare both the baseline and StyPath augmented models. We also generated Grad-CAM representations of the results which were assessed by an experienced nephropathologist; we used this qualitative analysis to elucidate on the assumptions being made by each model. Our results imply that our style-transfer augmentation technique improves histological classification performance (reducing error from 14.8% to 11.5%) and generalization ability.

StyPath：用于稳健的组织学图像分类的样式传递数据增强

即使对于有经验的肾病理学家来说，肾脏移植中抗体介导排斥（AMR）的分类仍然具有挑战性。部分原因是组织学组织染色分析通常以观察者之间的一致性低和可重复性差为特征。观察者之间存在分歧的牵连原因之一是病理实验室之间（和之内）的组织染色质量差异，以及档案切片的逐渐褪色。.. 染色颜色和强度的变化可能会使病理学家难以进行组织评估，最终影响他们描述相关形态特征的能力。能够根据肾脏组织学图像准确预测AMR状态对于改善患者的治疗和护理至关重要。我们提出了一种新颖的流水线，用于基于StyPath构建用于AMR分类的强大的深度神经网络，StyPath是一种组织学数据增强技术，利用轻量级样式转移算法作为减少样本特定偏差的手段。使用单个GTX TITAN V gpu和pytorch在1.84±0.03秒内生成每个图像，使其比其他流行的组织学数据增强技术更快。我们使用蒙特卡洛（MC）估计贝叶斯性能来评估模型，并生成不确定性的认知量度，以比较基线模型和StyPath增强模型。我们还生成了由经验丰富的肾脏病理学家评估的结果的Grad-CAM表示形式。我们使用这种定性分析来阐明每个模型所做的假设。我们的结果表明，我们的样式转移增强技术可提高组织学分类性能（将错误从14.8％减少到11.5％）和泛化能力。 （阅读更多）