论文标题
注意差距：扫描仪诱导的域转移组织病理学中的表示挑战
Mind the Gap: Scanner-induced domain shifts pose challenges for representation learning in histopathology
论文作者
论文摘要
组织病理学中的计算机辅助系统通常会受到各种域转移来源的挑战，这些转移的各种来源会极大地影响这些算法的性能。我们研究了使用自我监督的预训练来克服扫描仪诱导的域移位，以实现肿瘤分割的下游任务。为此，我们介绍了Barlow三重态，以从具有本地图像对应关系的多扫描仪数据集中学习扫描仪不变表示。我们表明，自我监管的预训练成功地使不同的扫描仪表示形式对齐，有趣的是，这只会对我们的下游任务带来有限的收益。因此，我们提供了有关扫描仪特征对下游应用的影响的见解，并有助于更好地理解为什么建立的自我监督方法尚未在组织病理学数据上显示出与自然图像相同的成功。
Computer-aided systems in histopathology are often challenged by various sources of domain shift that impact the performance of these algorithms considerably. We investigated the potential of using self-supervised pre-training to overcome scanner-induced domain shifts for the downstream task of tumor segmentation. For this, we present the Barlow Triplets to learn scanner-invariant representations from a multi-scanner dataset with local image correspondences. We show that self-supervised pre-training successfully aligned different scanner representations, which, interestingly only results in a limited benefit for our downstream task. We thereby provide insights into the influence of scanner characteristics for downstream applications and contribute to a better understanding of why established self-supervised methods have not yet shown the same success on histopathology data as they have for natural images.
