Scale-Aware Curriculum Learning for Ddata-Efficient Lung Nodule Detection with YOLOv11
This addresses data scarcity in clinical lung cancer diagnosis, though it appears incremental as an enhancement to existing curriculum learning methods.
The paper tackles the problem of lung nodule detection in chest CT scans with limited annotated data by proposing Scale Adaptive Curriculum Learning (SACL), which dynamically adjusts training based on data scale. Results show SACL achieves improvements of 4.6%, 3.5%, and 2.0% over baseline at 10%, 20%, and 50% of training data respectively.
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.