Vision-Language Generative Model for View-Specific Chest X-ray Generation
arxiv(2023)
Abstract
Synthetic medical data generation has opened up new possibilities in the
healthcare domain, offering a powerful tool for simulating clinical scenarios,
enhancing diagnostic and treatment quality, gaining granular medical knowledge,
and accelerating the development of unbiased algorithms. In this context, we
present a novel approach called ViewXGen, designed to overcome the limitations
of existing methods that rely on general domain pipelines using only radiology
reports to generate frontal-view chest X-rays. Our approach takes into
consideration the diverse view positions found in the dataset, enabling the
generation of chest X-rays with specific views, which marks a significant
advancement in the field. To achieve this, we introduce a set of specially
designed tokens for each view position, tailoring the generation process to the
user's preferences. Furthermore, we leverage multi-view chest X-rays as input,
incorporating valuable information from different views within the same study.
This integration rectifies potential errors and contributes to faithfully
capturing abnormal findings in chest X-ray generation. To validate the
effectiveness of our approach, we conducted statistical analyses, evaluating
its performance in a clinical efficacy metric on the MIMIC-CXR dataset. Also,
human evaluation demonstrates the remarkable capabilities of ViewXGen,
particularly in producing realistic view-specific X-rays that closely resemble
the original images.
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