  Histopathology image embedding is an active research area in computer vision.
Most of the embedding models exclusively concentrate on a specific
magnification level. However, a useful task in histopathology embedding is to
train an embedding space regardless of the magnification level. Two main
approaches for tackling this goal are domain adaptation and domain
generalization, where the target magnification levels may or may not be
introduced to the model in training, respectively. Although magnification
adaptation is a well-studied topic in the literature, this paper, to the best
of our knowledge, is the first work on magnification generalization for
histopathology image embedding. We use an episodic trainable domain
generalization technique for magnification generalization, namely Model
Agnostic Learning of Semantic Features (MASF), which works based on the Model
Agnostic Meta-Learning (MAML) concept. Our experimental results on a breast
cancer histopathology dataset with four different magnification levels show the
proposed method's effectiveness for magnification generalization.
