Key result
CMRformer, a multimodal learning framework, successfully learned representations from CMR image sequences and text reports, achieving superior performance in image retrieval and downstream disease classification compared to baseline models.
Why the study?
Conventional self-supervised vision-language models struggle with the complexity of CMR images and report alignment, and progress has been limited by the lack of large, publicly available datasets.
Answers are grounded in this paper and cite their sources.
CMRformer is a novel multimodal learning framework that successfully aligns CMR image sequences with text reports, enabling efficient image/report retrieval and disease classification.
Self-supervised learning is crucial for clinical imaging applications, given the lack of explicit labels in healthcare. However, conventional approaches that rely on precise vision-language alignment are not always feasible in complex clinical imaging modalities, such as cardiac magnetic resonance (CMR). CMR provides a comprehensive visualization of cardiac anatomy, physiology, and microstructure, making it challenging to interpret. Additionally, CMR reports require synthesizing information from sequences of images and different views, resulting in potentially weak alignment between the study and diagnosis report pair. To overcome these challenges, we propose CMRformer, a multimodal learning framework to jointly learn sequences of CMR images and associated cardiologist's reports. Moreover, one of the major obstacles to improving CMR study is the lack of large, publicly available datasets. To bridge this gap, we collected a large CMR dataset, which consists of 13,787 studies from clinical cases. By utilizing our proposed CMRformer and our collected dataset, we achieved remarkable performance in real-world clinical tasks, such as CMR image retrieval and diagnosis report retrieval. Furthermore, the learned representations are evaluated to be practically helpful for downstream applications, such as disease classification. Our work could potentially expedite progress in the CMR study and lead to more accurate and effective diagnosis and treatment.
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Qiu et al. (2023) studied Cardiovascular disease (n=13,786). CMRformer vs. Zero-shot and SimCLR models was evaluated on Retrieval performance (Recall@K) and downstream disease classification (Accuracy, AUC, F1). CMRformer, a multimodal learning framework, successfully learned representations from CMR image sequences and text reports, achieving superior performance in image retrieval and downstream disease classification compared to baseline models.
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