Position-Guided Prompt Learning for Anomaly Detection in Chest X-Rays
<|reference_start|>Position-Guided Prompt Learning for Anomaly Detection in Chest X-Rays: 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.<|reference_end|>
@article{sun2024position-guided,
    title={Position-Guided Prompt Learning for Anomaly Detection in Chest X-Rays},
    author={Zhichao Sun, Yuliang Gu, Yepeng Liu, Zerui Zhang, Zhou Zhao, Yongchao
  Xu},
    journal={arXiv preprint arXiv:2405.11976},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.11976},
    primaryClass={cs.CV}
}