Position-Guided Prompt Learning for Anomaly Detection in Chest X-Rays
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913397933604864 |
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| author | Sun, Zhichao Gu, Yuliang Liu, Yepeng Zhang, Zerui Zhao, Zhou Xu, Yongchao |
| author_facet | Sun, Zhichao Gu, Yuliang Liu, Yepeng Zhang, Zerui Zhao, Zhou Xu, Yongchao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_11976 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Position-Guided Prompt Learning for Anomaly Detection in Chest X-Rays Sun, Zhichao Gu, Yuliang Liu, Yepeng Zhang, Zerui Zhao, Zhou Xu, Yongchao Computer Vision and Pattern Recognition 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. |
| title | Position-Guided Prompt Learning for Anomaly Detection in Chest X-Rays |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.11976 |