  Prototypical networks aim to build intrinsically explainable models based on
the linear summation of concepts. However, important challenges remain in the
transparency, compactness, and meaningfulness of the explanations provided by
these models. This work demonstrates how frozen pre-trained ViT backbones can
be effectively turned into prototypical models for both general and
domain-specific tasks, in our case biomedical image classifiers. By leveraging
strong spatial features combined with a novel prototypical head, ProtoS-ViT
surpasses existing prototypical models showing strong performance in terms of
accuracy, compactness, and explainability. Model explainability is evaluated
through an extensive set of quantitative and qualitative metrics which serve as
a general benchmark for the development of prototypical models. Code is
available at https://github.com/hturbe/protosvit.
