This paper proposes leveraging vision-language pretraining on bone X-rays
paired with French reports to address downstream tasks of interest on bone
radiography. A practical processing pipeline is introduced to anonymize and
process French medical reports. Pretraining then consists in the
self-supervised alignment of visual and textual embedding spaces derived from
deep model encoders. The resulting image encoder is then used to handle various
downstream tasks, including quantification of osteoarthritis, estimation of
bone age on pediatric wrists, bone fracture and anomaly detection. Our approach
demonstrates competitive performance on downstream tasks, compared to
alternatives requiring a significantly larger amount of human expert
annotations. Our work stands as the first study to integrate French reports to
shape the embedding space devoted to bone X-Rays representations, capitalizing
on the large quantity of paired images and reports data available in an
hospital. By relying on generic vision-laguage deep models in a
language-specific scenario, it contributes to the deployement of vision models
for wider healthcare applications.

该研究提出了利用骨骼 X 射线与法语报告的视觉语言预训练来解决骨 X 射线影像学的下游任务。提出了一个实用的处理流程来对法国医学报告进行匿名化和处理。预训练包括来自深度模型编码器的视觉和文本嵌入空间的自我监督对齐。结果的图像编码器用于处理各种下游任务，包括骨关节炎的量化、儿童手腕骨龄的估计、骨折和异常检测。与需要大量人工专家注释的替代方法相比，我们的方法在下游任务上表现出竞争性能。我们的工作是第一项将法国报告整合到用于骨 X 射线表示的嵌入空间的研究，利用了医院中大量的成对图像和报告数据，以在特定语境中依赖通用视觉语言深度模型，为更广泛的医疗应用部署视觉模型作出贡献。