用少量标注数据实现高精度阿尔茨海默病早期检测
Enhancing Early Alzheimer Disease Detection through Big Data and Ensemble Few-Shot Learning
- 融合预训练CNN与原型网络的集成少样本学习方法
- 在两个数据集上准确率达99.72%至99.86%
- 适合医疗图像分析与小样本场景下的疾病检测研究者
阿尔茨海默病是一种严重脑部疾病，影响多个脑区并导致记忆损伤。由于标注医疗数据稀缺，准确检测面临挑战。本文提出一种基于原型网络（ProtoNet）的集成少样本学习方法，利用预训练卷积神经网络（CNN）作为编码器，增强医学图像特征表达能力，并结合类别感知损失与熵损失，提升疾病进展阶段分类精度。在Kaggle Alzheimer数据集和ADNI数据集上，分别达到99.72%和99.86%的准确率。实验结果表明，该方法优于现有先进方法，具备在真实场景中开展早期阿尔茨海默病检测的潜力。
原文摘要 · Abstract (English)
Alzheimer disease is a severe brain disorder that causes harm in various brain areas and leads to memory damage. The limited availability of labeled medical data poses a significant challenge for accurate Alzheimer disease detection. There is a critical need for effective methods to improve the accuracy of Alzheimer disease detection, considering the scarcity of labeled data, the complexity of the disease, and the constraints related to data privacy. To address this challenge, our study leverages the power of big data in the form of pre-trained Convolutional Neural Networks (CNNs) within the framework of Few-Shot Learning (FSL) and ensemble learning. We propose an ensemble approach based on a Prototypical Network (ProtoNet), a powerful method in FSL, integrating various pre-trained CNNs as encoders. This integration enhances the richness of features extracted from medical images. Our approach also includes a combination of class-aware loss and entropy loss to ensure a more precise classification of Alzheimer disease progression levels. The effectiveness of our method was evaluated using two datasets, the Kaggle Alzheimer dataset and the ADNI dataset, achieving an accuracy of 99.72% and 99.86%, respectively. The comparison of our results with relevant state-of-the-art studies demonstrated that our approach achieved superior accuracy and highlighted its validity and potential for real-world applications in early Alzheimer disease detection.
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