论文标题
以无监督的方式测量组织病理染色翻译的域移位
Towards Measuring Domain Shift in Histopathological Stain Translation in an Unsupervised Manner
论文作者
论文摘要
当使用不同的污渍或扫描仪，在污渍翻译期间等时，数字组织病理学的域移位可能会发生。对源数据训练的深神经网络可能无法很好地推广到经历了某些域移位的数据。朝着域转移朝着强大的转变的重要一步是检测和测量它的能力。本文表明，PixelCNN和域移位度量可用于检测和量化数字组织病理学中的域移位，并且它们与概括性能有很强的相关性。这些发现为一种机制铺平了道路，以推断出在看不见和未标记的目标数据上模型的平均性能（在源数据上受过训练）。
Domain shift in digital histopathology can occur when different stains or scanners are used, during stain translation, etc. A deep neural network trained on source data may not generalise well to data that has undergone some domain shift. An important step towards being robust to domain shift is the ability to detect and measure it. This article demonstrates that the PixelCNN and domain shift metric can be used to detect and quantify domain shift in digital histopathology, and they demonstrate a strong correlation with generalisation performance. These findings pave the way for a mechanism to infer the average performance of a model (trained on source data) on unseen and unlabelled target data.
