Explaining Deep Learning models is becoming increasingly important in the
face of daily emerging multimodal models, particularly in safety-critical
domains like medical imaging. However, the lack of detailed investigations into
the performance of explainability methods on these models is widening the gap
between their development and safe deployment. In this work, we analyze the
performance of various explainable AI methods on a vision-language model,
MedCLIP, to demystify its inner workings. We also provide a simple methodology
to overcome the shortcomings of these methods. Our work offers a different new
perspective on the explainability of a recent well-known VLM in the medical
domain and our assessment method is generalizable to other current and possible
future VLMs.

分析多模态模型的解释性性能以及提出克服这些方法的缺点的简单方法，并提供对医学领域中解释性的新观点，该评估方法具有普适性。

构想 MedCLIP：医学视觉语言模型的可解释性深入探究

Envisioning MedCLIP: A Deep Dive into Explainability for Medical  Vision-Language Models

Machine-learning (ML) shortcuts or spurious correlations are artifacts in
datasets that lead to very good training and test performance but severely
limit the model's generalization capability. Such shortcuts are insidious
because they go unnoticed due to good in-domain test performance. In this
paper, we explore the influence of different shortcuts and show that even
simple shortcuts are difficult to detect by explainable AI methods. We then
exploit this fact and design an approach to defend online databases against
crawlers: providers such as dating platforms, clothing manufacturers, or used
car dealers have to deal with a professionalized crawling industry that grabs
and resells data points on a large scale. We show that a deterrent can be
created by deliberately adding ML shortcuts. Such augmented datasets are then
unusable for ML use cases, which deters crawlers and the unauthorized use of
data from the internet. Using real-world data from three use cases, we show
that the proposed approach renders such collected data unusable, while the
shortcut is at the same time difficult to notice in human perception. Thus, our
proposed approach can serve as a proactive protection against illegitimate data
crawling.

设计了一种方法来通过加入机器学习快捷方式来防止网络爬虫，使得采集的数据无法使用，同时在人类感知中很难察觉，以此作为对非法数据采集的积极保护。