Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:4777-4780. doi: 10.1109/EMBC48229.2022.9871601.
The early and asymptomatic stages of Alzheimer's Disease (AD), such as mild cognitive impairment (MCI), are hard to classify, even by experienced physicians. Deep learning approaches, such as convolutional neural networks (CNNs), have been shown to help, achieving similar or even better results. Although these methods have the advantage that features are automatically extracted from images rather than handcrafted, they do not allow for incorporating medical knowledge. In this paper we propose curriculum learning (CL) strategies for CNNs designed to diagnose healthy subjects, MCI and AD, as a way to incorporate medical knowledge to boost the performance of the networks for early AD diagnosis. CL is a training strategy of the networks that tries to mimic the way humans, in this case doctors, learn. Several CL strategies were implemented and compared to commonly used baseline methods. The results show that they improve the performance, particularly that of MCI. Clinical relevance- This work shows that the use of CL strategies improve the diagnosis of AD, particularly at an early stage.
阿尔茨海默病（AD）的早期无症状阶段，如轻度认知障碍（MCI），很难进行分类，即使是有经验的医生也是如此。深度学习方法，如卷积神经网络（CNN），已被证明有助于提高分类准确率，甚至可以达到相似或更好的效果。尽管这些方法的优点是可以自动从图像中提取特征，而无需手工制作，但它们不允许纳入医学知识。在本文中，我们提出了一种用于 CNN 的课程学习（CL）策略，旨在诊断健康受试者、MCI 和 AD，以此将医学知识纳入网络，提高对早期 AD 的诊断性能。CL 是一种网络训练策略，旨在模仿人类（在这种情况下是医生）的学习方式。我们实施了几种 CL 策略，并与常用的基线方法进行了比较。结果表明，它们可以提高分类准确率，特别是对 MCI 的分类准确率。临床相关性——这项工作表明，使用 CL 策略可以改善 AD 的诊断，特别是在早期阶段。
