Abstract
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.
Abstract (translated)
胸部放射影像中的长尾肺部异常呈现出了巨大的诊断挑战。尽管最近基于扩散的方法在增强尾部病灶表示方面取得了进展，但罕见病变样本的稀缺限制了这些方法的生成能力，从而导致诊断精度不尽如人意。在这篇论文中，我们提出了一种新颖的数据合成流程，旨在利用大量常规正常X光片来扩充尾部病灶。具体而言，通过收集足够的正常样本训练一个可以生成正常X光图像的扩散模型。之后，该预训练的扩散模型被用来修复患病X光中的头部病灶，从而保留尾部类别作为增强的训练数据。此外，我们还提出了一种集成大型语言模型知识指导（LKG）模块和渐进式增量学习（PIL）策略的方法，以稳定图像修补微调过程。在公开的肺部数据集MIMIC和CheXpert上进行的全面评估表明，所提出的这种方法建立了新的性能基准。
URL
https://arxiv.org/abs/2512.20980
