Xplainer：从X射线观察到可解释的零样本诊断
Xplainer: From X-Ray Observations to Explainable Zero-Shot Diagnosis
- Technical University Munich（慕尼黑工业大学）
- Technical University of Munich（慕尼黑工业大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
Xplainer是一种新颖的可解释零样本诊断框架，通过将对比视觉语言模型适应于多标签医学诊断任务，利用描述性观察的概率来估计诊断可能性，在CheXpert和ChestX-ray14数据集上验证了其提升诊断性能和可解释性的有效性。
AI中文摘要：
从医学图像中进行自动诊断预测是支持临床决策的宝贵资源。然而，此类系统通常需要在大量标注数据上进行训练，而这些数据在医学领域往往很稀缺。零样本方法通过允许在不同临床发现的新设置下灵活适应而无需依赖标记数据，从而应对这一挑战。此外，为了将自动诊断整合到临床工作流程中，方法应当是透明且可解释的，以增加医疗专业人员的信任并促进正确性验证。在这项工作中，我们引入了Xplainer，这是一种用于临床环境中可解释零样本诊断的新颖框架。Xplainer将对比视觉语言模型的分类描述方法适应于多标签医学诊断任务。具体而言，我们不是直接预测诊断，而是提示模型对描述性观察的存在进行分类，这些观察是放射科医生在X射线扫描中会寻找的，并使用描述符概率来估计诊断的可能性。我们的模型在设计上是可解释的，因为最终的诊断预测直接基于对底层描述符的预测。我们在两个胸部X射线数据集CheXpert和ChestX-ray14上评估了Xplainer，并证明了其在改善零样本诊断的性能和可解释性方面的有效性。我们的结果表明，Xplainer提供了对决策过程更详细的理解，可以成为临床诊断的有价值工具。
英文摘要：
Automated diagnosis prediction from medical images is a valuable resource to support clinical decision-making. However, such systems usually need to be trained on large amounts of annotated data, which often is scarce in the medical domain. Zero-shot methods address this challenge by allowing a flexible adaption to new settings with different clinical findings without relying on labeled data. Further, to integrate automated diagnosis in the clinical workflow, methods should be transparent and explainable, increasing medical professionals' trust and facilitating correctness verification. In this work, we introduce Xplainer, a novel framework for explainable zero-shot diagnosis in the clinical setting. Xplainer adapts the classification-by-description approach of contrastive vision-language models to the multi-label medical diagnosis task. Specifically, instead of directly predicting a diagnosis, we prompt the model to classify the existence of descriptive observations, which a radiologist would look for on an X-Ray scan, and use the descriptor probabilities to estimate the likelihood of a diagnosis. Our model is explainable by design, as the final diagnosis prediction is directly based on the prediction of the underlying descriptors. We evaluate Xplainer on two chest X-ray datasets, CheXpert and ChestX-ray14, and demonstrate its effectiveness in improving the performance and explainability of zero-shot diagnosis. Our results suggest that Xplainer provides a more detailed understanding of the decision-making process and can be a valuable tool for clinical diagnosis.
