Accurate BAPL Score Classification of Brain PET Images Based on Convolutional Neural Networks with a Joint Discriminative Loss Function †

Applied Sciences

Kook Cho¹  Yen-Wei Chen²  Ryosuke Sato²  Yutaro Iwamoto²  Do-Young Kang³ 

[1] College of General Education, Dong-A University, Busan 49315, Korea;College of Information Science and Engineering, Ritsumeikan University, Shiga 603-8577, Japan;Institute of Convergence Bio-Health, Dong-A University, Busan 49201, Korea;

关键词: alzheimer’s disease;    deep learning;    convolutional neural network;    pet image;    brain amyloid plaque load (bapl) score;    coronal plane;    intra-loss function;    joint loss function;    mix-up;    data augmentation;   

DOI  :  10.3390/app10030965

来源: DOAJ

【 摘 要 】

Alzheimer’s disease (AD) is an irreversible progressive cerebral disease with most of its symptoms appearing after 60 years of age. Alzheimer’s disease has been largely attributed to accumulation of amyloid beta (Aβ), but a complete cure has remained elusive. 18F-Florbetaben amyloid positron emission tomography (PET) has been shown as a more powerful tool for understanding AD-related brain changes than magnetic resonance imaging and computed tomography. In this paper, we propose an accurate classification method for scoring brain amyloid plaque load (BAPL) based on deep convolutional neural networks. A joint discriminative loss function was formulated by adding a discriminative intra-loss function to the conventional (cross-entropy) loss function. The performance of the proposed joint loss function was compared with that of the conventional loss function in three state-of-the-art deep neural network architectures. The intra-loss function significantly improved the BAPL classification performance. In addition, we showed that the mix-up data augmentation method, originally proposed for natural image classification, was also useful for medical image classification.

【 授权许可】

Unknown