Learning domain-agnostic visual representation for computational pathology using medically-irrelevant style transfer augmentation

Suboptimal generalization of machine learning models on unseen data is a key challenge which hampers the clinical applicability of such models to medical imaging. Although various methods such as domain adaptation and domain generalization have evolved to combat this challenge, learning robust and generalizable representations is core to medical image understanding, and continues to be a problem. Here, we propose STRAP (Style TRansfer Augmentation for histoPathology), a form of data augmentation based on random style transfer from artistic paintings, for learning domain-agnostic visual representations in computational pathology. Style transfer replaces the low-level texture content of images with the uninformative style of randomly selected artistic paintings, while preserving high-level semantic content. This improves robustness to domain shift and can be used as a simple yet powerful tool for learning domain-agnostic representations. We demonstrate that STRAP leads to state-of-the-art performance, particularly in the presence of domain shifts, on a particular classification task of predicting microsatellite status in colorectal cancer using digitized histopathology images.

https://arxiv.org/abs/2102.01678

机器学习模型在未接触的数据上进行次优生成是一个具有挑战性的任务,这个任务执行的指令决定了医疗数据的可用性。尽管许多方法例如域适应以及域生成都可以解决上述挑战,然后学习一个鲁棒的且泛化能力强表示一直是医疗图像任务的核心。我们在本文中提出STRAP,一种基于随机风格迁移的数据扩增方法,它可以学习域不相关的计算病理学视觉表示。风格迁移用于将低层次纹理替换为统一的风格迁移图像,这样的风格迁移图像可以广泛地应用于域无关地表示中,我们展示了STRAP的SOTA性能在解决域飘逸的问题上,以及在大肠癌病理学图像的分类问题上。

发表评论

邮箱地址不会被公开。 必填项已用*标注