数据增广（data augmentation）在训练深度神经网络时的必要性解释

最新推荐文章于 2024-10-19 21:25:30 发布

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最新推荐文章于 2024-10-19 21:25:30 发布 · 1.1k 阅读

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#数据增广 #论文金句 #摘要

该文探讨了使用Residual U-Net进行左心室精确且鲁棒的分割，强调了多样化训练数据集和数据增强在防止过拟合中的重要性。通过随机选择图像并应用翻转、旋转和位移操作，增加输入数据的多样性，避免网络过度关注特定区域的特征。此外，提到随机弹性变形是训练少标注图像的关键。论文还提到了数据增强在教网络学习不变性和鲁棒性方面的作用。

Left-Ventricle Quantification Using Residual U-Net

Kerfoot E., Clough J., Oksuz I., Lee J., King A.P., Schnabel J.A. (2019) Left-Ventricle Quantification Using Residual U-Net. In: Pop M. et al. (eds) Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges. STACOM 2018. Lecture Notes in Computer Science, vol 11395. Springer, Cham. https://doi.org/10.1007/978-3-030-12029-0_40

3.1 Image Preprocessing

An accurate and robust segmentation network requires a large dataset so that it learns a general solution which can correctly segment images not seen in training, and does not become over-fitted to the input data. The key concep