Towards Measuring Domain Shift in Histopathological Stain Translation in an Unsupervised Manner
This addresses the issue of model robustness for medical imaging applications, but it is incremental as it builds on existing methods for domain shift measurement.
The paper tackled the problem of domain shift in digital histopathology by using PixelCNN and a domain shift metric to detect and quantify it, showing a strong correlation with generalization performance.
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.