In digital pathology, deep learning (DL) models for tasks such as segmentation or tissue classification are known to suffer from domain shifts due to different staining techniques. Stain adaptation aims to reduce the generalization error between different stains by training a model on source stains that generalizes to target stains. Despite the abundance of target stain data, a key challenge is the lack of annotations. To address this, we propose a joint training between artificially labeled and unlabeled data including all available stained images called Unsupervised Latent Stain Adaption (ULSA). Our method uses stain translation to enrich labeled source images with synthetic target images in order to increase supervised signals. Moreover, we leverage unlabeled target stain images using stain-invariant feature consistency learning. With ULSA we present a semi-supervised strategy for efficient stain adaption without access to annotated target stain data. Remarkably, ULSA is task agnostic in patch-level analysis for whole slide images (WSIs). Through extensive evaluation on external datasets, we demonstrate that ULSA achieves state-of-the-art (SOTA) performance in kidney tissue segmentation and breast cancer classification across a spectrum of staining variations. Our findings suggest that ULSA is an important framework towards stain adaption in digital pathology.

翻译：在数字病理学中，用于分割或组织分类等任务的深度学习模型已知会因不同染色技术而遭受域偏移问题。染色适应的目标是通过在源染色数据上训练模型，使其能够泛化至目标染色，从而减少不同染色间的泛化误差。尽管目标染色数据丰富，但关键挑战在于缺乏标注。为此，我们提出了一种联合训练方法，结合人工标注数据与包括所有可用染色图像在内的未标注数据，称为无监督潜在染色适应。我们的方法利用染色转换技术，通过合成目标图像来增强标注源图像，以增加监督信号。此外，我们利用染色不变特征一致性学习来挖掘未标注目标染色图像的潜力。ULSA提出了一种无需标注目标染色数据的半监督策略，可实现高效的染色适应。值得注意的是，在全切片图像的区块级分析中，ULSA具有任务无关性。通过在外部分割数据集上的广泛评估，我们证明ULSA在肾脏组织分割和乳腺癌分类任务中，针对一系列染色变异均达到了最先进的性能。我们的研究结果表明，ULSA是数字病理学中实现染色适应的重要框架。