展望MedCLIP：深入探讨医学视觉-语言模型的可解释性
Envisioning MedCLIP: A Deep Dive into Explainability for Medical Vision-Language Models
AI总结：
本文系统评估多种可解释AI方法在医学视觉-语言模型MedCLIP上的表现，揭示其内部机制并提出改进方法，为医学VLM的安全部署提供可推广的评估视角。
摘要：
随着多模态模型日益涌现，解释深度学习模型变得愈发重要，尤其是在医学影像等安全关键领域。然而，针对这些模型上可解释性方法性能的详细研究仍然缺乏，这正不断拉大模型开发与安全部署之间的差距。在本工作中，我们分析了多种可解释人工智能方法在视觉-语言模型MedCLIP上的表现，以揭示其内部工作机制。我们还提供了一种简单的方法论来克服这些方法的不足。我们的工作为医学领域中一个近期知名的视觉-语言模型的可解释性提供了不同的新视角，并且我们的评估方法可推广至其他当前及未来可能出现的视觉-语言模型。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
