论文标题
使用深度学习的腰椎MRI的自动Pfirrmann分级的更强基线
A Stronger Baseline For Automatic Pfirrmann Grading Of Lumbar Spine MRI Using Deep Learning
论文作者
论文摘要
本文解决了使用深度学习在腰椎MRI中对视觉特征进行分级的挑战。这种方法对于自动量化脊柱结构变化至关重要，这对于理解腰痛很有价值。最近的多项研究研究了不同的建筑设计，最近的成功归因于变压器体系结构的使用。在这项工作中，我们认为，凭借良好的三阶段管道包括语义细分，本地化和分类，卷积网络的表现优于最先进的方法。我们对人群队列中现有方法进行了消融研究，并报告了各个子组的性能概括。我们的代码可公开用于促进有关椎间盘变性和腰痛的研究。
This paper addresses the challenge of grading visual features in lumbar spine MRI using Deep Learning. Such a method is essential for the automatic quantification of structural changes in the spine, which is valuable for understanding low back pain. Multiple recent studies investigated different architecture designs, and the most recent success has been attributed to the use of transformer architectures. In this work, we argue that with a well-tuned three-stage pipeline comprising semantic segmentation, localization, and classification, convolutional networks outperform the state-of-the-art approaches. We conducted an ablation study of the existing methods in a population cohort, and report performance generalization across various subgroups. Our code is publicly available to advance research on disc degeneration and low back pain.
