[论文解读] Detection of Alzheimers Disease from MRI using Convolutional Neural Network with Tensorflow

GuruRaj Awate, Sunil L. Bangare+2 more|arXiv (Cornell University)|Jun 26, 2018

Brain Tumor Detection and Classification被引用 41

一句话总结

该论文提出使用在 TensorFlow 中实现的卷积神经网络（CNN）来从 MRI 扫描中分类阿尔茨海默病，训练数据来自 OASIS 数据集。

ABSTRACT

Nowadays, due to tremendous improvements in high performance computing, it has become easier to train Neural Networks. We intend to take advantage of this situation and apply this technology in solving real world problems. There was a need for automatic diagnosis certain diseases from medical images that could help a doctor and radiologist for further action towards treating the illness. We chose Alzheimer disease for this purpose. Alzheimer disease is the leading cause of dementia and memory loss. Alzheimer disease, it is caused by atrophy of the certain brain regions and by brain cell death. MRI scans reveal this information but atrophy regions are different for different people which makes the diagnosis a little trickier and often gets miss-diagnosed by doctors and radiologists. The Dataset used for this project is provided by OASIS, which contains over 400 subjects 100 of which having mild to severe dementia and is supplemented by MMSE and CDR standards of diagnosis in the same context. Enter CNN, Convolutional Neural Networks are a hybrid of Kernel Convolutions and Neural Networks. Kernel Convolutions is a technique that uses filters to recognize and segment images based on features. Neural Networks consist of neurons which are loosely based on human brains neuron which represents a single classifier and interconnected by weights, have different biases and are activated by some activation functions. By using Convolutional Neural Networks, the problem can be solved with minimal error rate. The technologies we intend to use are libraries like CUDA CuDNN for making use of GPU and its multiple cores-parallel computing to train models while giving us high performance.

研究动机与目标

• 促进从医学影像自动诊断疾病，以帮助医生和放射科医师。
• 将 CNN 应用于 MRI 数据以检测阿尔茨海默病。
• 利用 GPU 加速（CUDA CuDNN）以实现高效训练。
• 展示混合图像分析技术在脑萎缩模式中的可行性。

提出的方法

• 使用 TensorFlow 在 MRI 扫描上训练卷积神经网络。
• 利用 CUDA CuDNN 以利用 GPU 并行性来实现更快的训练。
• 依赖 OASIS 数据集，该数据集包含用于诊断的 MMSE 和 CDR 标准。
• 解释核心 CNN 概念：卷积核/过滤器、特征图，以及用于图像分类的学习权值。

实验结果

研究问题

• RQ1 使用 OASIS 数据集在 MRI 数据上训练的 CNN 能否在阿尔茨海默病与非痴呆病例之间做出准确区分？
• RQ2 GPU 加速的 CNN 训练在此医疗影像诊断任务中的表现如何？
• RQ3 MMSE 和 CDR 标准在验证 CNN 预测中的作用是什么？
• RQ4 由于个体脑区萎缩变异性带来的局限性和诊断挑战是什么？

主要发现

• 本研究展示了使用 TensorFlow 的 CNN 处理来自 MRI 扫描的阿尔茨海默病检测。
• OASIS 数据集提供超过 400 名受试者，其中约 100 名患有痴呆，同时提供 MMSE 和 CDR 诊断。
• 该方法利用 GPU 加速（CUDA CuDNN）来提高训练性能。