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

TL;DR

This paper demonstrates that multi-domain models, which incorporate diverse medical imaging data, significantly improve out-of-distribution generalization and performance in data-limited scenarios compared to specialized models.

Contribution

It introduces a multi-domain modeling approach for medical image analysis, leveraging diverse imaging modalities and views to enhance generalization and robustness.

Findings

Multi-domain models outperform specialized models in out-of-distribution scenarios.

Multi-domain models improve accuracy by up to 8% in organ recognition.

Diverse data integration enhances model robustness in healthcare applications.

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, 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.