Stain variation is a unique challenge associated with automated analysis of
digital pathology. Numerous methods have been developed to improve the
robustness of machine learning methods to stain variation, but comparative
studies have demonstrated limited benefits to performance. Moreover, methods to
handle stain variation were largely developed for H&E stained data, with
evaluation generally limited to classification tasks. Here we propose Stain
Consistency Learning, a novel framework combining stain-specific augmentation
with a stain consistency loss function to learn stain colour invariant
features. We perform the first, extensive comparison of methods to handle stain
variation for segmentation tasks, comparing ten methods on Masson's trichrome
and H&E stained cell and nuclei datasets, respectively. We observed that stain
normalisation methods resulted in equivalent or worse performance, while stain
augmentation or stain adversarial methods demonstrated improved performance,
with the best performance consistently achieved by our proposed approach. The
code is available at: this https URL

数字病理学中的染色变异是自动化分析的一个独特挑战，本研究提出了一种新的框架，利用染色的特定增强和染色一致性损失函数来学习染色色彩不变的特征，在细胞和细胞核数据集上，与其他方法相比，该方法表现最佳。

染色一致性学习：处理数字病理学分割中的染色变异

Stain Consistency Learning: Handling Stain Variation for Automatic  Digital Pathology Segmentation

Staining reveals the micro structure of the aspirate while creating
histopathology slides. Stain variation, defined as a chromatic difference
between the source and the target, is caused by varying characteristics during
staining, resulting in a distribution shift and poor performance on the target.
The goal of stain normalization is to match the target's chromatic distribution
to that of the source. However, stain normalisation causes the underlying
morphology to distort, resulting in an incorrect diagnosis. We propose FUSION,
a new method for promoting stain-adaption by adjusting the model to the target
in an unsupervised test-time scenario, eliminating the necessity for
significant labelling at the target end. FUSION works by altering the target's
batch normalization statistics and fusing them with source statistics using a
weighting factor. The algorithm reduces to one of two extremes based on the
weighting factor. Despite the lack of training or supervision, FUSION surpasses
existing equivalent algorithms for classification and dense predictions
(segmentation), as demonstrated by comprehensive experiments on two public
datasets.

FUSION 是一种无监督的、通过调整测试目标的批次标准化统计数据并使用加权因子与源统计数据融合以促进染色适应性的新方法，它能够在不需要标注网络的情况下，胜过现有的等效算法，在两个公共数据集上进行了全面的实验。

FUSION: 基于融合归一化统计的全面无监督测试时染色适应

FUSION: Fully Unsupervised Test-Time Stain Adaptation via Fused  Normalization Statistics

Stain variation is a phenomenon observed when distinct pathology laboratories
stain tissue slides that exhibit similar but not identical color appearance.
Due to this color shift between laboratories, convolutional neural networks
(CNNs) trained with images from one lab often underperform on unseen images
from the other lab. Several techniques have been proposed to reduce the
generalization error, mainly grouped into two categories: stain color
augmentation and stain color normalization. The former simulates a wide variety
of realistic stain variations during training, producing stain-invariant CNNs.
The latter aims to match training and test color distributions in order to
reduce stain variation. For the first time, we compared some of these
techniques and quantified their effect on CNN classification performance using
a heterogeneous dataset of hematoxylin and eosin histopathology images from 4
organs and 9 pathology laboratories. Additionally, we propose a novel
unsupervised method to perform stain color normalization using a neural
network. Based on our experimental results, we provide practical guidelines on
how to use stain color augmentation and stain color normalization in future
computational pathology applications.

本文研究组比较了针对组织切片染色变异的两种技术：染色色彩扩增和染色色彩规范化，并在异质性数据集上量化了它们对卷积神经网络分类性能的影响，并提出了一种使用神经网络进行无监督染色色彩规范化的新方法。