Anomaly detection in chest X-rays is a critical task. Most methods mainly model the distribution of normal images, and then regard significant deviation from normal distribution as anomaly. Recently, CLIP-based methods, pre-trained on a large number of medical images, have shown impressive performance on zero/few-shot downstream tasks. In this paper, we aim to explore the potential of CLIP-based methods for anomaly detection in chest X-rays. Considering the discrepancy between the CLIP pre-training data and the task-specific data, we propose a position-guided prompt learning method. Specifically, inspired by the fact that experts diagnose chest X-rays by carefully examining distinct lung regions, we propose learnable position-guided text and image prompts to adapt the task data to the frozen pre-trained CLIP-based model. To enhance the model's discriminative capability, we propose a novel structure-preserving anomaly synthesis method within chest x-rays during the training process. Extensive experiments on three datasets demonstrate that our proposed method outperforms some state-of-the-art methods. The code of our implementation is available at https://github.com/sunzc-sunny/PPAD.

翻译：胸部X光异常检测是一项关键任务。大多数方法主要对正常图像的分布进行建模，然后将显著偏离正常分布的图像视为异常。最近，基于CLIP的方法在大规模医学图像上预训练后，在零样本/少样本下游任务中展现出优越性能。本文旨在探索基于CLIP的方法在胸部X光异常检测中的潜力。考虑到CLIP预训练数据与任务特定数据之间的差异，我们提出了一种位置引导的提示学习方法。具体而言，受专家通过仔细检查不同肺区域来诊断胸部X光的启发，我们提出了可学习的位置引导文本和图像提示，将任务数据适配到冻结的预训练CLIP模型中。为了增强模型区分能力，我们在训练过程中提出了一种新颖的、保留结构的胸部X光异常合成方法。在三个数据集上的广泛实验表明，我们的方法优于某些现有最优方法。我们的实现代码可在 https://github.com/sunzc-sunny/PPAD 获取。