论文标题
帕金森氏病检测，基于ILSVRC模型的合奏体系结构
Parkinson's Disease Detection with Ensemble Architectures based on ILSVRC Models
论文作者
论文摘要
在这项工作中，我们使用磁共振（MR）T1图像探索各种神经网络体系结构，以识别帕金森氏病（PD），这是最常见的神经退行性和运动障碍之一。我们提出了三个组合体系结构，结合了Imagenet大规模视觉识别挑战（ILSVRC）的一些获胜的卷积神经网络模型。我们所有提议的体系结构的表现都超过了从MR图像中检测PD的现有方法，达到95 \％检测准确性。我们还发现，当我们使用与PD无关的ImageNet数据集上预测的模型构建集合体系结构时，与没有任何先前培训的模型相比，检测性能要好得多。我们的发现表明，当没有或不足的培训数据不足时，一个有希望的方向。
In this work, we explore various neural network architectures using Magnetic Resonance (MR) T1 images of the brain to identify Parkinson's Disease (PD), which is one of the most common neurodegenerative and movement disorders. We propose three ensemble architectures combining some winning Convolutional Neural Network models of ImageNet Large Scale Visual Recognition Challenge (ILSVRC). All of our proposed architectures outperform existing approaches to detect PD from MR images, achieving upto 95\% detection accuracy. We also find that when we construct our ensemble architecture using models pretrained on the ImageNet dataset unrelated to PD, the detection performance is significantly better compared to models without any prior training. Our finding suggests a promising direction when no or insufficient training data is available.
