Multi-domain improves classification in out-of-distribution and data-limited scenarios for medical image analysis.

ABSTRACT: 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, a

SUBMITTER: Ozkan E 

PROVIDER: S-EPMC11487066 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature