In this work, we perform an in-depth analysis of the visualisation methods
implemented in two popular self-explaining models for visual classification
based on prototypes - ProtoPNet and ProtoTree. Using two fine-grained datasets
(CUB-200-2011 and Stanford Cars), we first show that such methods do not
correctly identify the regions of interest inside of the images, and therefore
do not reflect the model behaviour. Secondly, using a deletion metric, we
demonstrate quantitatively that saliency methods such as Smoothgrads or PRP
provide more faithful image patches. We also propose a new relevance metric
based on the segmentation of the object provided in some datasets (e.g.
CUB-200-2011) and show that the imprecise patch visualisations generated by
ProtoPNet and ProtoTree can create a false sense of bias that can be mitigated
by the use of more faithful methods. Finally, we discuss the implications of
our findings for other prototype-based models sharing the same visualisation
method.

本文深入分析了两种流行的自说明模型 ——ProtoPNet 和 ProtoTree 的可视化方法，并使用细粒度数据集进行了验证。通过删除指标的定量证明，证明了 Smoothgrads 或 PRP 等显著性方法提供了更准确的图像补丁，并提出了一种基于物体分割的相关性度量。本研究对其他使用相同可视化方法的原型模型的影响进行了讨论。

原型图像分类中补丁可视化的合理性检查和改进

Sanity checks and improvements for patch visualisation in prototype-based image classification

Application of interpretable machine learning techniques on medical datasets
facilitate early and fast diagnoses, along with getting deeper insight into the
data. Furthermore, the transparency of these models increase trust among
application domain experts. Medical datasets face common issues such as
heterogeneous measurements, imbalanced classes with limited sample size, and
missing data, which hinder the straightforward application of machine learning
techniques. In this paper we present a family of prototype-based (PB)
interpretable models which are capable of handling these issues. The models
introduced in this contribution show comparable or superior performance to
alternative techniques applicable in such situations. However, unlike ensemble
based models, which have to compromise on easy interpretation, the PB models
here do not. Moreover we propose a strategy of harnessing the power of
ensembles while maintaining the intrinsic interpretability of the PB models, by
averaging the model parameter manifolds. All the models were evaluated on a
synthetic (publicly available dataset) in addition to detailed analyses of two
real-world medical datasets (one publicly available). Results indicated that
the models and strategies we introduced addressed the challenges of real-world
medical data, while remaining computationally inexpensive and transparent, as
well as similar or superior in performance compared to their alternatives.

本文介绍了一种基于原型的可解释模型，在应对特定医学数据集的问题时，不仅能够具备可解释性，还能保持高性能。通过对一个合成和两个真实医学数据集的详细分析，这些模型得以证明在处理医学数据方面的有效性，可成为医学诊断提供支持。