探索使用YOLOv8在结肠镜图像中进行深度学习息肉检测的迁移学习
Exploring Transfer Learning for Deep Learning Polyp Detection in Colonoscopy Images Using YOLOv8
AI总结：
本文研究在七个不同数据集上预训练YOLOv8n模型对结肠镜息肉检测任务中迁移学习效果的影响，发现使用具有领域相关特征的数据集进行预训练能显著提升模型性能。
摘要：
深度学习方法在目标检测任务中表现出色；然而，在训练数据有限的情况下学习特定领域应用的能力仍然是一个重大挑战。迁移学习技术通过利用在相关数据集上预训练获得的知识来解决这一问题，从而能够针对新任务实现更快、更高效的学习。寻找合适的预训练数据集对于决定迁移学习的成功和整体模型性能起着至关重要的作用。在本文中，我们研究了在七个不同数据集上预训练YOLOv8n模型的影响，并评估了将其迁移到息肉检测任务时的有效性。我们比较了包含多样化物体的大型通用数据集是否优于具有与息肉相似特征的细分小众数据集。此外，我们还评估了数据集规模对迁移学习有效性的影响。在息肉数据集上的实验表明，在相关数据集上预训练的模型始终优于从头开始训练的模型，这突显了在具有共享领域特定特征的数据集上进行预训练的益处。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
