Due to the sensitive nature of medicine, it is particularly important and
highly demanded that AI methods are explainable. This need has been recognised
and there is great research interest in xAI solutions with medical
applications. However, there is a lack of user-centred evaluation regarding the
actual impact of the explanations. We evaluate attribute- and prototype-based
explanations with the Proto-Caps model. This xAI model reasons the target
classification with human-defined visual features of the target object in the
form of scores and attribute-specific prototypes. The model thus provides a
multimodal explanation that is intuitively understandable to humans thanks to
predefined attributes. A user study involving six radiologists shows that the
explanations are subjectivly perceived as helpful, as they reflect their
decision-making process. The results of the model are considered a second
opinion that radiologists can discuss using the model's explanations. However,
it was shown that the inclusion and increased magnitude of model explanations
objectively can increase confidence in the model's predictions when the model
is incorrect. We can conclude that attribute scores and visual prototypes
enhance confidence in the model. However, additional development and repeated
user studies are needed to tailor the explanation to the respective use case.

基于 Proto-Caps 模型的 AI 方法和 xAI 解决方案提供了可理解的多模态解释，对放射科医师的决策过程有帮助，但需要进一步开发和重复用户研究以适应相应的用例。

评估医疗分类模型的属性和原型的可解释性

Evaluating the Explainability of Attributes and Prototypes for a Medical  Classification Model

Interpretability is often an essential requirement in medical imaging.
Advanced deep learning methods are required to address this need for
explainability and high performance. In this work, we investigate whether
additional information available during the training process can be used to
create an understandable and powerful model. We propose an innovative solution
called Proto-Caps that leverages the benefits of capsule networks, prototype
learning and the use of privileged information. Evaluating the proposed
solution on the LIDC-IDRI dataset shows that it combines increased
interpretability with above state-of-the-art prediction performance. Compared
to the explainable baseline model, our method achieves more than 6 % higher
accuracy in predicting both malignancy (93.0 %) and mean characteristic
features of lung nodules. Simultaneously, the model provides case-based
reasoning with prototype representations that allow visual validation of
radiologist-defined attributes.

探索了一种名为 Proto-Caps 的创新解决方案，它能够利用额外的训练过程中可用的信息来创建一个易理解且功能强大的模型，通过在 LIDC-IDRI 数据集上的评估，该方法结合了更强的可解释性和先进的预测性能，相较于可解释的基线模型，在预测恶性程度（93.0%）和肺结节的平均特征方面，我们的方法提高了超过 6% 的准确率，并同时提供了基于案例的推理和可视化验证放射学定义的特征。