Background and objective

Alzheimer’s Disease (AD) is a chronic and fatal neurodegenerative disease with progressive impairment of memory. Brain structural magnetic resonance imaging (sMRI) has been widely applied as important biomarkers of AD. Various machine learning approaches, especially deep learning-based models, have been proposed for the early diagnosis of AD and monitoring the disease progression on sMRI data. However, the requirement for a large number of training images still hinders the extensive usage of AD diagnosis. In addition, due to the similarities in human whole-brain structure, finding the subtle brain changes is essential to extract discriminative features from limited sMRI data effectively.

Methods

In this work, we proposed two types of contrastive losses with paired sMRIs to promote the diagnostic performance using group categories (G-CAT) and varying subject mini-mental state examination (S-MMSE) information, respectively. Specifically, G-CAT contrastive loss layer was used to learn the closer feature representation from sMRIs with the same categories, while ranking information from S-MMSE assists the model to explore subtle changes between individuals.

Results

The model was trained on ADNI-1. Comparison with baseline methods was performed on MIRIAD and ADNI-2. For the classification task on MIRIAD, S-MMSE achieves 93.5% of accuracy, 96.6% of sensitivity, and 94.9% of specificity, respectively. G-CAT and S-MMSE both reach remarkable performance in terms of classification sensitivity and specificity respectively. Comparing with state-of-the-art methods, we found this proposed method could achieve comparable results with other approaches.

Conclusion

The proposed model could extract discriminative features under whole-brain similarity. Extensive experiments also support the accuracy of this model, i.e., it provides better ability to identify uncertain samples, especially for the classification task of subjects with MMSE in 22–27. Source code is freely available at https://github.com/fengduqianhe/ADComparative.

中文翻译：

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使用配对结构 MRI 进行对比性损失的早期阿尔茨海默病诊断

背景和目的

阿尔茨海默病 (AD) 是一种慢性、致命的神经退行性疾病，伴有进行性记忆障碍。脑结构磁共振成像 (sMRI) 作为 AD 的重要生物标志物已被广泛应用。已经提出了各种机器学习方法，特别是基于深度学习的模型，用于 AD 的早期诊断和监测 sMRI 数据的疾病进展。然而，对大量训练图像的需求仍然阻碍了 AD 诊断的广泛应用。此外，由于人类全脑结构的相似性，发现细微的大脑变化对于有效地从有限的 sMRI 数据中提取判别特征至关重要。

方法

在这项工作中，我们提出了两种类型的对比损失与配对 sMRI，以分别使用组类别 (G-CAT) 和不同的主题微型精神状态检查 (S-MMSE) 信息来提高诊断性能。具体来说，G-CAT 对比损失层用于从具有相同类别的 sMRI 中学习更接近的特征表示，而来自 S-MMSE 的排名信息有助于模型探索个体之间的细微变化。

结果

该模型是在 ADNI-1 上训练的。在 MIRIAD 和 ADNI-2 上进行了与基线方法的比较。对于 MIRIAD 上的分类任务，S-MMSE 分别达到了 93.5% 的准确度、96.6% 的灵敏度和 94.9% 的特异性。G-CAT 和 S-MMSE 都分别在分类灵敏度和特异性方面达到了卓越的性能。与最先进的方法相比，我们发现这种提出的方​​法可以达到与其他方法相当的结果。

结论

所提出的模型可以提取全脑相似性下的判别特征。大量的实验也支持了该模型的准确性，即它提供了更好的识别不确定样本的能力，特别是对于 22-27 具有 MMSE 的受试者的分类任务。源代码可在 https://github.com/fengduqianhe/ADComparative 免费获得。