bibtext: @InProceedings{ Sun_PositionGuided_MICCAI2024,
                   author = { Sun, Zhichao and Gu, Yuliang and Liu, Yepeng and Zhang, Zerui and Zhao, Zhou and Xu, Yongchao },
                   title = { { Position-Guided Prompt Learning for Anomaly Detection in Chest X-Rays } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15001 },
                   month = {October},
                   pages = { pending },
                }
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.
title: Position-Guided Prompt Learning for Anomaly Detection in Chest X-Rays
authors: Sun, Zhichao Gu, Yuliang Liu, Yepeng Zhang, Zerui Zhao, Zhou Xu, Yongchao