X射线洞察力释放：开创多标签长尾数据增强领域
X-ray Insights Unleashed: Pioneering the Enhancement of Multi-Label Long-Tail Data
AI总结：
本文提出一种基于扩散模型和LKG模块的X光数据合成方法，通过正常X光片生成长尾病变数据，提升肺部异常诊断的性能。
摘要：
胸部X光片中的长尾肺部异常给诊断带来了严峻挑战。尽管最近在基于扩散的方法中取得了进展，用于增强长尾病变表示，但稀有病变示例的匮乏限制了这些方法的生成能力，从而导致诊断精度不理想。在本文中，我们提出了一种新的数据合成管道，旨在利用大量常规正常X光片来增强长尾病变。具体而言，收集足够的正常样本来训练一个能够生成正常X光图像的扩散模型。此预训练的扩散模型随后用于修复患病X光片中的头部病变，从而将长尾类别作为增强的训练数据。此外，我们还提出了将大型语言模型知识指导（LKG）模块与渐进增量学习（PIL）策略相结合，以稳定修复微调过程。在公共肺部数据集MIMIC和CheXpert上的全面评估表明，所提出的方法在性能上设立了新的基准。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
