作者：Chantal Pellegrini Matthias Keicher Ege Özsoy Petra Jiraskova Rickmer Braren Nassir Navab
根据医学图像进行自动诊断预测是支持临床决策的宝贵资源。然而，这样的系统通常需要基于大量的注释数据，而这些数据在医学领域通常是稀缺的。零样本方法通过允许灵活适应具有不同临床发现的新设置，而不依赖于标记的数据，解决了这一挑战。此外，要将自动化诊断集成到临床工作流程中，方法应该是透明和可解释的，增加医学专业人员的信任，并促进正确性验证。在这项工作中，我们介绍了Xplainer，一种在临床环境中进行可解释的零样本诊断的新框架。Xplainer将对比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。具体地说，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，这是一位二元醇学家会看到的
Automated diagnosis prediction from medical images is a valuable resource tosupport clinical decision-making. However, such systems usually need to betrained on large amounts of annotated data, which often is scarce in themedical domain. Zero-shot methods address this challenge by allowing a flexibleadaption to new settings with different clinical findings without relying onlabeled data. Further, to integrate automated diagnosis in the clinicalworkflow, methods should be transparent and explainable, increasing medicalprofessionals’ trust and facilitating correctness verification. In this work,we introduce Xplainer, a novel framework for explainable zero-shot diagnosis inthe clinical setting. Xplainer adapts the classification-by-descriptionapproach of contrastive vision-language models to the multi-label medicaldiagnosis task. Specifically, instead of directly predicting a diagnosis, weprompt the model to classify the existence of descriptive observations, which aradiologist would look for on an X-Ray scan, and use the descriptorprobabilities to estimate the likelihood of a diagnosis. Our model isexplainable by design, as the final diagnosis prediction is directly based onthe prediction of the underlying descriptors. We evaluate Xplainer on two chestX-ray datasets, CheXpert and ChestX-ray14, and demonstrate its effectiveness inimproving the performance and explainability of zero-shot diagnosis. Ourresults suggest that Xplainer provides a more detailed understanding of thedecision-making process and can be a valuable tool for clinical diagnosis.
论文链接：http://arxiv.org/pdf/2303.13391v1
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