针对标注数据少的肺结节检测，提出动态调整训练策略的新方法。
Scale-Aware Curriculum Learning for Ddata-Efficient Lung Nodule Detection with YOLOv11
- 根据数据量自动调整训练节奏和难例注入
- 在10%数据下比基线提升4.6%的检测准确率
- 适合标注资源有限的医疗机构使用
胸部CT中的肺结节检测对早期肺癌诊断至关重要，但现有深度学习方法在标注数据有限的临床场景中表现受限。传统课程学习策略在数据稀缺时效果不佳。本文提出尺度自适应课程学习（SACL），通过自适应训练轮次调度、难样本注入和尺度感知优化三个机制，动态适应不同数据规模。在LUNA25数据集上以YOLOv11为基线模型进行评估，结果显示：在完整数据集上SACL与静态课程学习在mAP50上相当；而在仅10%、20%、50%训练数据下，分别取得4.6%、3.5%、2.0%的性能提升。该方法无需修改模型结构，可在不同数据规模下实现稳定训练，为标注资源有限的医疗单位提供实用解决方案。
原文摘要 · Abstract (English)
Lung nodule detection in chest CT is crucial for early lung cancer diagnosis, yet existing deep learning approaches face challenges when deployed in clinical settings with limited annotated data. While curriculum learning has shown promise in improving model training, traditional static curriculum strategies fail in data-scarce scenarios. We propose Scale Adaptive Curriculum Learning (SACL), a novel training strategy that dynamically adjusts curriculum design based on available data scale. SACL introduces three key mechanisms:(1) adaptive epoch scheduling, (2) hard sample injection, and (3) scale-aware optimization. We evaluate SACL on the LUNA25 dataset using YOLOv11 as the base detector. Experimental results demonstrate that while SACL achieves comparable performance to static curriculum learning on the full dataset in mAP50, it shows significant advantages under data-limited conditions with 4.6%, 3.5%, and 2.0% improvements over baseline at 10%, 20%, and 50% of training data respectively. By enabling robust training across varying data scales without architectural modifications, SACL provides a practical solution for healthcare institutions to develop effective lung nodule detection systems despite limited annotation resources.
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