Curriculum learning for early Alzheimer's Disease diagnosis.

Abstract

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.