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首页> 外文会议>International Work-Conference on Artificial Neural Networks >Unsupervised Learning as a Complement to Convolutional Neural Network Classification in the Analysis of Saccadic Eye Movement in Spino-Cerebellar Ataxia Type 2
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Unsupervised Learning as a Complement to Convolutional Neural Network Classification in the Analysis of Saccadic Eye Movement in Spino-Cerebellar Ataxia Type 2

机译：无监督学习作为卷积神经网络分类的补充，在脊髓缺血型共济失调中的扫视眼球运动中的分析2

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This paper aims at assessing spino-cerebellar type 2 ataxia by classifying electrooculography records into registers corresponding to healthy, presymptomatic and ill individuals. The primary used technique is the convolutional neural network applied to the time series of eye movements, called saccades. The problem is exceptionally hard, though, because the recorded saccadic movements for presymptomatic cases often do not substantially differ from those of healthy individuals. Precisely this distinction is of the utmost clinical importance, since early intervention on presymptomatic patients can ameliorate symptoms or at least slow their progression. Yet, each register contains a number of saccades that, although not consistent with the current label, have not been considered indicative of another class by the examining physicians. As a consequence, an unsupervised learning mechanism may be more suitable to handle this form of misclassification. Thus, our proposal introduces the k-means approach and the SOM method, as complementary techniques to analyse the time series. The three techniques operating in tandem lead to a well performing solution to this diagnosis problem.
机译：本文旨在通过将电胶记录记录分类为对应于健康，假设和生病的人的寄存器来评估脊髓灰质2型共济失调。主要使用技术是卷积神经网络，其应用于眼球运动的时间序列，称为扫视。然而，问题非常艰难，因为假设患者的记录的扫视运动通常与健康个体的遗传性没有显着不同。正是这种区别是最大的临床重要性，因为早期干预假设患者可以改善症状或至少缓慢他们的进展。然而，每个寄存器包含许多扫视，尽管与当前标签不一致，但尚未被认为是检查医生的另一个类。因此，无监督的学习机制可能更适合处理这种形式的错误分类。因此，我们的提案介绍了K-Means方法和SOM方法，作为分析时间序列的互补技术。三种技术在串联中运行导致对该诊断问题的良好表现良好的解决方案。

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