面向计算病理学的无监督潜在染色适应
Unsupervised Latent Stain Adaptation for Computational Pathology
AI总结：
本文提出无监督潜在染色适应（ULSA）框架，通过染色转换生成合成目标图像并结合染色不变特征一致性学习，在无目标染色标注的情况下实现肾脏组织分割和乳腺癌分类的最先进性能。
摘要：
在计算病理学中，用于分割或组织分类等任务的深度学习（DL）模型已知会因不同染色技术而产生域偏移。染色适应旨在通过在源染色上训练一个能泛化到目标染色的模型，来减少不同染色之间的泛化误差。尽管目标染色数据丰富，但一个关键挑战是缺乏标注。为了解决这一问题，我们提出在人工标注数据和包括所有可用染色图像在内的未标注数据之间进行联合训练，称为无监督潜在染色适应（ULSA）。我们的方法利用染色转换来丰富已标注的源图像，生成合成的目标图像，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利用未标注的目标染色图像。通过ULSA，我们提出了一种半监督策略，在无法获得已标注目标染色数据的情况下实现高效的染色适应。值得注意的是，ULSA在整张切片图像（WSIs）的块级分析中与任务无关。通过在外部数据集上的广泛评估，我们证明ULSA在一系列染色变异下的肾脏组织分割和乳腺癌分类中达到了最先进（SOTA）的性能。我们的研究结果表明，ULSA是计算病理学中染色适应的重要框架。
英文摘要：
In computational 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 Adaptation (ULSA). Our method uses stain translation to enrich labeled source images with synthetic target images in order to increase the 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 adaptation 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 for stain adaptation in computational pathology.
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