用YOLOv8n研究肠镜图像中息肉检测的迁移学习效果
Exploring Transfer Learning for Deep Learning Polyp Detection in Colonoscopy Images Using YOLOv8
- 在7个不同数据集上预训练YOLOv8n，评估迁移效果
- 相关领域数据预训练显著优于从零开始训练
- 适配息肉特征的数据集更利于模型泛化
深度学习在目标检测任务中表现优异，但在有限标注数据下学习特定领域应用仍具挑战。迁移学习通过利用相关数据集上的预训练知识，可加速新任务的学习过程。本文研究了将YOLOv8n模型在7个不同数据集上进行预训练对结直肠镜图像息肉检测任务的影响，比较了通用大而杂的数据集与具有类似息肉特征的专用小数据集的优劣，并评估了数据集规模对迁移效果的影响。实验结果表明，在相关领域数据上预训练的模型性能持续优于从零开始训练的模型，证明了共享领域特征的数据集在迁移学习中的关键作用。
原文摘要 · Abstract (English)
Deep learning methods have demonstrated strong performance in objection tasks; however, their ability to learn domain-specific applications with limited training data remains a significant challenge. Transfer learning techniques address this issue by leveraging knowledge from pre-training on related datasets, enabling faster and more efficient learning for new tasks. Finding the right dataset for pre-training can play a critical role in determining the success of transfer learning and overall model performance. In this paper, we investigate the impact of pre-training a YOLOv8n model on seven distinct datasets, evaluating their effectiveness when transferred to the task of polyp detection. We compare whether large, general-purpose datasets with diverse objects outperform niche datasets with characteristics similar to polyps. In addition, we assess the influence of the size of the dataset on the efficacy of transfer learning. Experiments on the polyp datasets show that models pre-trained on relevant datasets consistently outperform those trained from scratch, highlighting the benefit of pre-training on datasets with shared domain-specific features.
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