Abstract
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.
Abstract (translated)
医学图像的自动诊断预测是一种重要的资源，以支持临床决策。然而，这种系统通常需要从大量的标注数据中进行训练，这在医学领域中往往是缺乏的。零样本方法解决了这个问题，它可以在没有标记数据的情况下灵活适应不同的临床发现设置，无需依赖标签数据。进一步，将自动诊断集成到临床工作流程中，方法应该透明和可解释，增加医务人员的信任，并方便正确性验证。在这个项目中，我们介绍了Xplainer，一个可在临床环境中解释零样本诊断的新框架。Xplainer将竞争视觉语言模型的描述分类方法应用于多标签医学诊断任务。具体来说，我们不再直接预测诊断，而是促使模型分类描述观察的存在，这是放射科医生在X射线扫描中会寻找的描述观察，并使用描述概率估计诊断的可能性。我们的模型是设计可解释的，因为其最终诊断预测直接基于底层描述预测。我们评估了 CheXpert和 chestX-ray14两个心电学数据集，并证明了Xplainer在改善零样本诊断性能和解释性方面的效力。我们的结果表明，Xplainer提供了更详细的理解决策过程，可以成为临床诊断的宝贵工具。
URL
https://arxiv.org/abs/2303.13391
