Assessment of Scanner Domain Shifts in Deep Multiple Instance Learning

Abstract

Deep multiple instance learning is a popular method for classifying whole slide images, but it remains unclear how robust such models are against scanner-induced domain shifts. In this work, we studied this problem based on the classification of the mutational status of the c-Kit gene from whole slide images of canine mast cell tumors obtained with three different scanners. Furthermore, we investigated the possibility of utilizing image augmentation during feature extraction to overcome domain shifts. Our findings suggest that a notable domain shift exists between models trained on different scanners. Nevertheless, the use of image augmentations during feature extraction failed to address this domain shift and had no positive effect on in-domain performance.