Multi-Prototype Few-shot Learning in Histopathology.

The ability to adapt quickly to a new task or data distribution based on only a few examples is a challenge in AI and highly relevant for various domains. In digital pathology, slight variations in the scanning and staining process can lead to a distribution shift that provokes significant performance degradation of classical neural networks for tasks like tissue cartography where a reliable classification is essential. To overcome this problem, we propose a few-shot learning technique, specifically a k-means extension of Prototypical Networks, to train a highly flexible model that adapts to new, unseen scanner data based on only a few examples. We evaluate our approach on a multi-scanner database comprising a total amount of 356 annotated whole slide images digitized by a base scanner for training and additional five different scanners for evaluation. We verify our method's effectiveness by comparing it to a classically trained benchmark and Prototypical Networks, both trained on the same data. A particular focus for us is to investigate the support set, used for adapting the prototypes, to provide recommended actions for digital pathology. The best results are obtained by employing multiple prototypes per class, calculated from a distributed support set, and domain-specific data augmentation. This results in 86.9 - 88.2% accuracy for a classification task of seven tissue classes on unseen, shifted data from the automated scanners, which is almost equal to the accuracy on the indistribution data of 89.2%.