用隐式神经表示评估医学影像质量，更省内存且精度不降
Beyond Pixels: Medical Image Quality Assessment with Implicit Neural Representations
- 用隐式神经表示替代像素，连续压缩影像数据
- 在ACDC数据集上实现与传统方法相当的质检效果
- 适合追求轻量化与高可扩展性的医疗影像分析场景
伪影严重影响医学影像的诊断准确性和下游分析。虽然基于图像的伪影检测方法有效，但常依赖预处理步骤，导致信息损失和高内存占用，限制了分类模型的可扩展性。本文提出使用隐式神经表示（INRs）进行影像质量评估。INRs能以紧凑、连续的方式表征医学影像，自然适应分辨率和尺寸变化，同时降低内存开销。我们构建了深度权重空间网络、图神经网络和关系注意力变换器，直接在INRs上操作以实现质量评估。方法在包含合成伪影模式的ACDC数据集上进行了验证，证明其在参数更少的情况下仍能达到与传统方法相当的性能。
原文摘要 · Abstract (English)
Artifacts pose a significant challenge in medical imaging, impacting diagnostic accuracy and downstream analysis. While image-based approaches for detecting artifacts can be effective, they often rely on preprocessing methods that can lead to information loss and high-memory-demand medical images, thereby limiting the scalability of classification models. In this work, we propose the use of implicit neural representations (INRs) for image quality assessment. INRs provide a compact and continuous representation of medical images, naturally handling variations in resolution and image size while reducing memory overhead. We develop deep weight space networks, graph neural networks, and relational attention transformers that operate on INRs to achieve image quality assessment. Our method is evaluated on the ACDC dataset with synthetically generated artifact patterns, demonstrating its effectiveness in assessing image quality while achieving similar performance with fewer parameters.
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