  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.
