用正常X光生成稀有肺部病灶图像，提升罕见病诊断准确率
X-ray Insights Unleashed: Pioneering the Enhancement of Multi-Label Long-Tail Data
- 用大量正常X光训练扩散模型，反向修复病变图像中的罕见病灶
- 在MIMIC和CheXpert数据集上显著提升罕见病检测性能
- 结合大模型知识与渐进学习，稳定生成过程避免灾难性遗忘
胸部X光中长尾分布的肺部异常诊断难度高。尽管基于扩散模型的方法在增强尾部病灶表征方面取得进展，但罕见病灶样本不足限制了生成能力，导致诊断精度不理想。本文提出一种新型数据合成流程，利用大量常规正常X光图像增强尾部病灶。首先收集充足正常样本训练扩散模型生成正常图像；随后利用该模型对病灶图像中的头部病灶进行修复，保留尾部类别作为增强训练数据。此外，引入大语言模型知识引导（LKG）模块与渐进式增量学习（PIL）策略，稳定修复过程中的微调。在公开肺部数据集MIMIC和CheXpert上的全面评估表明，该方法达到新基准性能。
原文摘要 · Abstract (English)
Long-tailed pulmonary anomalies in chest radiography present formidable diagnostic challenges. Despite the recent strides in diffusion-based methods for enhancing the representation of tailed lesions, the paucity of rare lesion exemplars curtails the generative capabilities of these approaches, thereby leaving the diagnostic precision less than optimal. In this paper, we propose a novel data synthesis pipeline designed to augment tail lesions utilizing a copious supply of conventional normal X-rays. Specifically, a sufficient quantity of normal samples is amassed to train a diffusion model capable of generating normal X-ray images. This pre-trained diffusion model is subsequently utilized to inpaint the head lesions present in the diseased X-rays, thereby preserving the tail classes as augmented training data. Additionally, we propose the integration of a Large Language Model Knowledge Guidance (LKG) module alongside a Progressive Incremental Learning (PIL) strategy to stabilize the inpainting fine-tuning process. Comprehensive evaluations conducted on the public lung datasets MIMIC and CheXpert demonstrate that the proposed method sets a new benchmark in performance.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。