  Multiple Instance Learning (MIL) has become the predominant approach for
classification tasks on gigapixel histopathology whole slide images (WSIs).
Within the MIL framework, single WSIs (bags) are decomposed into patches
(instances), with only WSI-level annotation available. Recent MIL approaches
produce highly informative bag level representations by utilizing the
transformer architecture's ability to model the dependencies between instances.
However, when applied to high magnification datasets, problems emerge due to
the large number of instances and the weak supervisory learning signal. To
address this problem, we propose to additionally train transformers with a
novel Bag Embedding Loss (BEL). BEL forces the model to learn a discriminative
bag-level representation by minimizing the distance between bag embeddings of
the same class and maximizing the distance between different classes. We
evaluate BEL with the Transformer architecture TransMIL on two publicly
available histopathology datasets, BRACS and CAMELYON17. We show that with BEL,
TransMIL outperforms the baseline models on both datasets, thus contributing to
the clinically highly relevant AI-based tumor classification of histological
patient material.
