论文标题
用于计算病理的联合染色归一化
Federated Stain Normalization for Computational Pathology
论文作者
论文摘要
尽管近年来，深层联邦学习引起了很多关注，但主要是在自然图像的背景下取得的进展，几乎没有计算病理学。但是，深层联合学习是创建反映许多实验室数据多样性的数据集的机会。此外，数据集构建的努力可以划分为许多。不幸的是，现有算法不能轻易应用于计算病理学，因为以前的工作以实验室的数据分布必须相似。这是一个不太可能的假设，主要是因为不同的实验室具有不同的染色方式。作为解决方案，我们提出了Bottergan，这是一种生成模型，可以在计算许多实验室的染色样式上，并可以以隐私性的方式进行培训，以培养计算病理学中的联合学习。与现有联合学习算法相比，我们基于PESO分割数据集构建了一个基于PESO分割数据集的异质多机构数据集，并将IOU提高42 \％。可以从https://github.com/meclabtuda/bottlegan获得Bottergan的实施。
Although deep federated learning has received much attention in recent years, progress has been made mainly in the context of natural images and barely for computational pathology. However, deep federated learning is an opportunity to create datasets that reflect the data diversity of many laboratories. Further, the effort of dataset construction can be divided among many. Unfortunately, existing algorithms cannot be easily applied to computational pathology since previous work presupposes that data distributions of laboratories must be similar. This is an unlikely assumption, mainly since different laboratories have different staining styles. As a solution, we propose BottleGAN, a generative model that can computationally align the staining styles of many laboratories and can be trained in a privacy-preserving manner to foster federated learning in computational pathology. We construct a heterogenic multi-institutional dataset based on the PESO segmentation dataset and improve the IOU by 42\% compared to existing federated learning algorithms. An implementation of BottleGAN is available at https://github.com/MECLabTUDA/BottleGAN
