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.

翻译：原型网络旨在基于概念的线性求和构建本质可解释的模型。然而，这些模型所提供的解释在透明度、紧凑性和意义性方面仍存在重大挑战。本工作展示了如何将冻结的预训练ViT主干网络有效转化为适用于通用任务及领域特定任务（本文以生物医学图像分类器为例）的原型模型。通过结合强大的空间特征与一种新颖的原型头部，ProtoS-ViT超越了现有原型模型，在准确性、紧凑性和可解释性方面均表现出优异性能。模型的可解释性通过一套广泛的定量与定性评估指标进行衡量，这些指标可作为原型模型开发的通用基准。代码发布于 https://github.com/hturbe/protosvit。