Generated synthetic data in medical research can substitute privacy and security-sensitive data with a large-scale curated dataset, reducing data collection and annotation costs. As part of this effort, we propose UniXGen, a unified chest X-ray and report generation model, with the following contributions. First, we design a unified model for bidirectional chest X-ray and report generation by adopting a vector quantization method to discretize chest X-rays into discrete visual tokens and formulating both tasks as sequence generation tasks. Second, we introduce several special tokens to generate chest X-rays with specific views that can be useful when the desired views are unavailable. Furthermore, UniXGen can flexibly take various inputs from single to multiple views to take advantage of the additional findings available in other X-ray views. We adopt an efficient transformer for computational and memory efficiency to handle the long-range input sequence of multi-view chest X-rays with high resolution and long paragraph reports. In extensive experiments, we show that our unified model has a synergistic effect on both generation tasks, as opposed to training only the task-specific models. We also find that view-specific special tokens can distinguish between different views and properly generate specific views even if they do not exist in the dataset, and utilizing multi-view chest X-rays can faithfully capture the abnormal findings in the additional X-rays. The source code is publicly available at: https://github.com/ttumyche/UniXGen.

翻译：在医学研究中，生成的合成数据能够替代涉及隐私和安全敏感的大规模策展数据集，从而降低数据收集和标注成本。为此，我们提出UniXGen——一个统一的胸部X光片与报告生成模型，其贡献如下：首先，通过采用向量量化方法将胸部X光片离散化为视觉标记，并将两项任务均建模为序列生成任务，我们设计了一个用于双向胸部X光片与报告生成的统一模型。其次，我们引入若干特殊标记以生成特定视角的胸部X光片，这在所需视角数据缺失时尤为实用。此外，UniXGen可灵活接受从单视角到多视角的多种输入，从而充分利用其他X光片中包含的额外发现。为处理高分辨率多视角胸部X光片及长段落报告带来的长序列输入，我们采用了一种计算与内存效率兼具的高效Transformer架构。大量实验表明，相较于仅训练特定任务模型，我们的统一模型对两项生成任务均具有协同增强效应。我们还发现，视角专属的特殊标记能有效区分不同视角，并在数据集缺失特定视角的情况下正确生成相应图像；而利用多视角胸部X光片可更准确地捕捉额外X光片中的异常发现。源代码已公开于：https://github.com/ttumyche/UniXGen。