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

• URL: http://arxiv.org/abs/2405.11976v2
• Date: Thu, 20 Jun 2024 03:13:45 GMT
• Title: Position-Guided Prompt Learning for Anomaly Detection in Chest X-Rays
• Authors: Zhichao Sun, Yuliang Gu, Yepeng Liu, Zerui Zhang, Zhou Zhao, Yongchao Xu,
• Abstract summary: Anomaly detection in chest X-rays is a critical task. Recently, CLIP-based methods, pre-trained on a large number of medical images, have shown impressive performance on zero/few-shot downstream tasks. We propose a position-guided prompt learning method to adapt the task data to the frozen CLIP-based model.
• Score: 46.78926066405227
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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