在标签有限时，精准刻画关键解剖结构比模型复杂度更重要。
Which Anatomy Matters Under Limited Labels? A Data-Efficient Anatomy-Aware Benchmark for Cardiac Pathology Prediction
- 用心脏各腔室分割特征构建解剖感知表示
- 少样本下解剖表示性能优于模型复杂度提升
- 适合资源受限的医疗场景下的病理预测研究
许多医学影像任务在标签稀缺和计算资源受限的条件下进行，但尚不清楚性能提升主要来自更复杂的模型，还是更准确的临床解剖结构表示。本文基于公开的ACDC MRI数据集，构建了一个5类心脏病理预测的低数据解剖感知基准。利用右心室、心肌和左心室的分割衍生患者描述符，对比线性、核方法和树模型在解剖特异性和多结构表示上的表现。结果表明，在标签有限的情况下，表示能力远超模型复杂度的影响。这提示在资源受限的医疗环境中，识别并精确表示最具信息量的解剖结构，比单纯增加模型复杂度更为关键。
原文摘要 · Abstract (English)
Numerous medical imaging problems must be solved under limited labels and constrained compute, yet it remains unclear whether performance gains are driven mainly by more expressive models or by better representation of clinically meaningful anatomy. We study this question through a low-data anatomy-aware benchmark for 5-class cardiac pathology prediction on the public ACDC MRI dataset. Using segmentation-derived patient descriptors from the right ventricle, myocardium, and left ventricle, we compare anatomy-specific and multi-structure representations across linear, kernel, and tree-based classifiers. We find that under limited label settings, representation dominates complexity. These results suggest that in resource-constrained healthcare settings, identifying and representing the most informative anatomy may matter more than the increasing complexity of the model alone.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。