Fake It Till You Make It: Using Synthetic Data and Domain Knowledge for Improved Text-Based Learning for LGE Detection

TL;DR

This study introduces a novel approach for detecting cardiac LGE in MRI images using only clinical report text, synthetic data augmentation, and domain knowledge, achieving improved performance with limited data.

Contribution

The paper presents a new method leveraging synthetic data, standardized image orientation, and captioning loss for LGE detection without extensive image annotations.

Findings

Synthetic data augmentation improves detection accuracy.

Standardized image orientation enhances model alignment.

Pretraining vision encoder boosts performance.

Abstract

Detection of hyperenhancement from cardiac LGE MRI images is a complex task requiring significant clinical expertise. Although deep learning-based models have shown promising results for the task, they require large amounts of data with fine-grained annotations. Clinical reports generated for cardiac MR studies contain rich, clinically relevant information, including the location, extent and etiology of any scars present. Although recently developed CLIP-based training enables pretraining models with image-text pairs, it requires large amounts of data and further finetuning strategies on downstream tasks. In this study, we use various strategies rooted in domain knowledge to train a model for LGE detection solely using text from clinical reports, on a relatively small clinical cohort of 965 patients. We improve performance through the use of synthetic data augmentation, by systematically creating scar images and associated text. In addition, we standardize the orientation of the images in an anatomy-informed way to enable better alignment of spatial and text features. We also use a captioning loss to enable fine-grained supervision and explore the effect of pretraining of the vision encoder on performance. Finally, ablation studies are carried out to elucidate the contributions of each design component to the overall performance of the model.