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.

在数字病理学中，通过使用未标注的目标染色图像和标注的源染色图像，结合染色转换和无监督学习方法 ULSA，我们提出了一种半监督策略来实现有效的染色适应，从而在肾脏组织分割和乳腺癌分类方面实现了最先进的性能。