Position-Guided Prompt Learning for Anomaly Detection in Chest X-Rays

TL;DR

This paper introduces a position-guided prompt learning approach leveraging CLIP for improved anomaly detection in chest X-rays, incorporating expert-inspired region focus and anomaly synthesis to enhance discriminative power.

Contribution

It proposes a novel position-guided prompt learning method with structure-preserving anomaly synthesis to adapt CLIP for chest X-ray anomaly detection.

Findings

Outperforms state-of-the-art methods on three datasets

Effective use of position-guided prompts improves detection accuracy

Anomaly synthesis enhances model discriminability

Abstract

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.