论文标题
图像语言可解释模型类似于领域专业知识的能力
The Ability of Image-Language Explainable Models to Resemble Domain Expertise
论文作者
论文摘要
视觉和语言（V+L）模型的最新进展对医疗保健领域产生了有希望的影响。但是，这样的模型难以解释如何以及为什么做出特定决定。此外，模型透明度和域专业知识的参与是机器学习模型进入现场的关键成功因素。在这项工作中，我们研究了局部替代解释性技术来克服黑盒深度学习模型的问题。我们探讨了使用本地替代物与基础V+L结合使用本地替代物与域专业知识相似的可行性，以生成多模式的视觉和语言解释。我们证明，这种解释可以作为指导现场数据科学家和机器学习工程师的指导模型培训的有益反馈。
Recent advances in vision and language (V+L) models have a promising impact in the healthcare field. However, such models struggle to explain how and why a particular decision was made. In addition, model transparency and involvement of domain expertise are critical success factors for machine learning models to make an entrance into the field. In this work, we study the use of the local surrogate explainability technique to overcome the problem of black-box deep learning models. We explore the feasibility of resembling domain expertise using the local surrogates in combination with an underlying V+L to generate multi-modal visual and language explanations. We demonstrate that such explanations can serve as helpful feedback in guiding model training for data scientists and machine learning engineers in the field.
