Multi-domain improves out-of-distribution and data-limited scenarios for
  medical image analysis
Ece Ozkan Xavier Boix
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. Recently,
foundation models have been proposed, which combine data from various domains
and demonstrate excellent generalization capabilities. Building upon this, this
work introduces 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. We refer
to this approach as multi-domain model and compare its performance to that of
specialized models. 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 shared information across domains, enhancing the overall
outcomes significantly. To illustrate, for organ recognition, multi-domain
model can enhance accuracy by up to 10% compared to conventional specialized
models.