用超分辨率技术提升低质超声图像诊断精度，助力资源匮乏地区智能诊疗。
Recovering Diagnostic Value: Super-Resolution-Aided Echocardiographic Classification in Resource-Constrained Imaging
- 用SRGAN和SRResNet增强低质量超声图像，提升分类效果。
- 在CAMUS数据集上，复杂任务准确率提升超过15%。
- 适合基层医疗、远程诊断等资源受限场景使用。
在资源受限的临床环境中，自动心脏影像分析常因超声图像质量差而受阻，影响下游诊断模型效果。尽管超分辨率（SR）技术在MRI和CT中表现良好，但在广泛使用但噪声严重的超声成像领域仍研究不足。本文基于公开的CAMUS数据集，按图像质量分层，评估了两个临床相关任务：较简单的两腔与四腔心视图分类，以及更复杂的收缩末期与舒张末期相位分类。采用SRGAN和SRResNet两种主流深度学习超分辨率模型对低质量2D超声图像进行增强，结果表明性能显著提升，尤其以SRResNet表现最优，且具备更高计算效率。研究证实，超分辨率能有效恢复劣质超声图像的诊断价值，为资源受限环境下的AI辅助诊疗提供了可行方案。
原文摘要 · Abstract (English)
Automated cardiac interpretation in resource-constrained settings (RCS) is often hindered by poor-quality echocardiographic imaging, limiting the effectiveness of downstream diagnostic models. While super-resolution (SR) techniques have shown promise in enhancing magnetic resonance imaging (MRI) and computed tomography (CT) scans, their application to echocardiography-a widely accessible but noise-prone modality-remains underexplored. In this work, we investigate the potential of deep learning-based SR to improve classification accuracy on low-quality 2D echocardiograms. Using the publicly available CAMUS dataset, we stratify samples by image quality and evaluate two clinically relevant tasks of varying complexity: a relatively simple Two-Chamber vs. Four-Chamber (2CH vs. 4CH) view classification and a more complex End-Diastole vs. End-Systole (ED vs. ES) phase classification. We apply two widely used SR models-Super-Resolution Generative Adversarial Network (SRGAN) and Super-Resolution Residual Network (SRResNet), to enhance poor-quality images and observe significant gains in performance metric-particularly with SRResNet, which also offers computational efficiency. Our findings demonstrate that SR can effectively recover diagnostic value in degraded echo scans, making it a viable tool for AI-assisted care in RCS, achieving more with less.
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