Diffusion-Based User-Guided Data Augmentation for Coronary Stenosis Detection

TL;DR

This paper introduces a diffusion-based data augmentation method for coronary stenosis detection that enhances model performance, especially with limited data, by generating realistic, user-guided lesion images to improve diagnosis accuracy.

Contribution

The study presents a novel diffusion model-based inpainting technique for data augmentation, enabling realistic lesion generation with user-controlled severity, improving coronary stenosis detection and classification.

Findings

Superior performance on large-scale datasets

Effective with limited training data

Maintains high detection accuracy

Abstract

Coronary stenosis is a major risk factor for ischemic heart events leading to increased mortality, and medical treatments for this condition require meticulous, labor-intensive analysis. Coronary angiography provides critical visual cues for assessing stenosis, supporting clinicians in making informed decisions for diagnosis and treatment. Recent advances in deep learning have shown great potential for automated localization and severity measurement of stenosis. In real-world scenarios, however, the success of these competent approaches is often hindered by challenges such as limited labeled data and class imbalance. In this study, we propose a novel data augmentation approach that uses an inpainting method based on a diffusion model to generate realistic lesions, allowing user-guided control of severity. Extensive evaluation on lesion detection and severity classification across various synthetic dataset sizes shows superior performance of our method on both a large-scale in-house dataset and a public coronary angiography dataset. Furthermore, our approach maintains high detection and classification performance even when trained with limited data, highlighting its clinical importance in improving the assessment of severity of stenosis and optimizing data utilization for more reliable decision support.