Towards a vision foundation model for comprehensive assessment of Cardiac MRI

TL;DR

This paper introduces a self-supervised vision foundation model trained on 36 million CMR images, enabling accurate, robust, and resource-efficient comprehensive cardiac assessment across multiple tasks with minimal labeled data.

Contribution

The work presents a unified, self-supervised foundation model for CMR that improves performance and data efficiency across diverse clinical tasks, unlike task-specific models.

Findings

Achieves comparable performance to state-of-the-art models on clinical tasks.

Demonstrates significant improvements in few-shot learning scenarios.

Provides a resource-efficient, unified framework for CMR analysis.

Abstract

Cardiac magnetic resonance imaging (CMR), considered the gold standard for noninvasive cardiac assessment, is a diverse and complex modality requiring a wide variety of image processing tasks for comprehensive assessment of cardiac morphology and function. Advances in deep learning have enabled the development of state-of-the-art (SoTA) models for these tasks. However, model training is challenging due to data and label scarcity, especially in the less common imaging sequences. Moreover, each model is often trained for a specific task, with no connection between related tasks. In this work, we introduce a vision foundation model trained for CMR assessment, that is trained in a self-supervised fashion on 36 million CMR images. We then finetune the model in supervised way for 9 clinical tasks typical to a CMR workflow, across classification, segmentation, landmark localization, and pathology detection. We demonstrate improved accuracy and robustness across all tasks, over a range of available labeled dataset sizes. We also demonstrate improved few-shot learning with fewer labeled samples, a common challenge in medical image analyses. We achieve an out-of-box performance comparable to SoTA for most clinical tasks. The proposed method thus presents a resource-efficient, unified framework for CMR assessment, with the potential to accelerate the development of deep learning-based solutions for image analysis tasks, even with few annotated data available.