Current machine learning methods for medical image analysis primarily focus on developing models tailored for their specific tasks, utilizing data within their target domain. These specialized models tend to be data-hungry and often exhibit limitations in generalizing to out-of-distribution samples. In this work, we show that employing models that incorporate multiple domains instead of specialized ones significantly alleviates the limitations observed in specialized models. We refer to this approach as multi-domain model and compare its performance to that of specialized models. For this, we introduce the incorporation of diverse medical image domains, including different imaging modalities like X-ray, MRI, CT, and ultrasound images, as well as various viewpoints such as axial, coronal, and sagittal views. Our findings underscore the superior generalization capabilities of multi-domain models, particularly in scenarios characterized by limited data availability and out-of-distribution, frequently encountered in healthcare applications. The integration of diverse data allows multi-domain models to utilize information across domains, enhancing the overall outcomes substantially. To illustrate, for organ recognition, multi-domain model can enhance accuracy by up to 8% compared to conventional specialized models.

翻译：当前医学影像分析的机器学习方法主要集中于针对特定任务开发模型，并利用其目标领域内的数据。这些专用模型往往需要大量数据，且在泛化至分布外样本时通常表现出局限性。本研究表明，采用融合多领域而非单一领域的模型能显著缓解专用模型所面临的局限性。我们将此方法称为多领域模型，并将其性能与专用模型进行比较。为此，我们引入了多样化的医学影像领域，包括X射线、磁共振成像、计算机断层扫描和超声图像等不同成像模态，以及轴状面、冠状面和矢状面等多种视角。我们的研究结果突显了多领域模型卓越的泛化能力，尤其是在医疗应用中常见的数据可用性受限和分布外场景下。多样化数据的整合使多领域模型能够跨领域利用信息，从而大幅提升整体性能。例如在器官识别任务中，多领域模型相较于传统专用模型可将准确率提升高达8%。