论文标题
用嘈杂标签从少量医疗数据中深入学习：一种元学习方法
Deep Learning from Small Amount of Medical Data with Noisy Labels: A Meta-Learning Approach
论文作者
论文摘要
由于深度神经网络，计算机视觉系统最近取得了重大飞跃。但是，这些系统需要正确标记大型数据集才能进行适当的培训，这对于医疗应用很难获得。医疗应用中标记噪声的两个主要原因是数据的高复杂性和专家的意见相互矛盾。此外，医学成像数据集通常很小，这使每个数据在学习中非常重要。结果，如果无法正确处理，标记噪声会大大降低性能。因此，在本文中提出了使用元学习范式的标签 - 努力学习算法。对所提出的解决方案进行了对早产（ROP）数据集的视网膜病变的测试，其标签噪声非常高68％。结果表明，所提出的算法显着改善了在嘈杂标签的存在下分类算法的性能。
Computer vision systems recently made a big leap thanks to deep neural networks. However, these systems require correctly labeled large datasets in order to be trained properly, which is very difficult to obtain for medical applications. Two main reasons for label noise in medical applications are the high complexity of the data and conflicting opinions of experts. Moreover, medical imaging datasets are commonly tiny, which makes each data very important in learning. As a result, if not handled properly, label noise significantly degrades the performance. Therefore, a label-noise-robust learning algorithm that makes use of the meta-learning paradigm is proposed in this article. The proposed solution is tested on retinopathy of prematurity (ROP) dataset with a very high label noise of 68%. Results show that the proposed algorithm significantly improves the classification algorithm's performance in the presence of noisy labels.
