X-ray Insights Unleashed: Pioneering the Enhancement of Multi-Label Long-Tail Data

• URL: http://arxiv.org/abs/2512.20980v1
• Date: Wed, 24 Dec 2025 06:14:55 GMT
• Title: X-ray Insights Unleashed: Pioneering the Enhancement of Multi-Label Long-Tail Data
• Authors: Xinquan Yang, Jinheng Xie, Yawen Huang, Yuexiang Li, Huimin Huang, Hao Zheng, Xian Wu, Yefeng Zheng, Linlin Shen,
• Abstract summary: Long-tailed pulmonary anomalies in chest radiography present formidable diagnostic challenges.<n>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.<n>We propose a novel data synthesis pipeline designed to augment tail lesions utilizing a copious supply of conventional normal X-rays.
• Score: 86.52299247918637
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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