Saman Sarraf, Ghassem Tofighi, Classification of Alzheimer's Disease Structural MRI Data by Deep Learning Convolutional Neural Networks, /abs/1607.06583
• 面积测量忽略了皮层厚度 对于萎缩程度的影响
• 体积测量
• 轻度AD海马萎缩约为15%22%，严重者可达40%
• 内嗅皮层、旁嗅皮层及颞极皮 层在AD均显示萎缩，以内嗅 皮层萎缩最明显
• 海马及内嗅皮层萎缩可以早期 诊断AD，体积测定诊断AD的 准确度约85%-94%
深度学习——疾病的分类
• 脑：脑血管病，神经退行性疾病（阿尔茨海默病，帕金森病，癫 痫），精神疾病（抑郁症，精神分裂症），脑瘤
In this work we propose an assessment system that abides practical usability constraints and applies deep learning to differentiate disease state in data collected in naturalistic settings. Based on a large data-set collected from 34 people with PD we illustrate that deep learning outperforms other approaches in generalisation performance, despite the unreliable labelling characteristic for this problem setting, and how such systems could improve current clinical practice.
关于我：哥伦比亚大学工作
关于我：哥伦比亚大学生物医学信息中心
生物医学信息分析； 采用贝叶斯模型；
关于我：OSU脑科学中心
关于我：OSU脑科学中心
MRI成像与图像分析的训练，西门子公司； 采用贝叶斯模型； 导师：Mark A. Pitt教授，Jay Myung教授，Zhong-Lin Lu教授，认知科学
In this paper, we used convolutional neural network to classify Alzheimer’s brain from normal healthy brain. The importance of classifying this kind of medical data is to potentially develop a predict model or system in order to recognize the type disease from normal subjects or to estimate the stage of the disease. Using Convolutional Neural Network (CNN) and the famous architecture LeNet-5, we successfully classified structural MRI data of Alzheimer’s subjects from normal controls where the accuracy of test data on trained data reached 98.84%.
亡，脑组脑织神经缺细失胞。死如亡左，图脑所组示织，缺失阿。尔如茨左海
图所示， 阿尔茨海默病将导致严重
默病将导的致脑萎严缩重的脑萎缩；
B
临床上以记临忆床障上以碍记、忆失障语碍、、失失用语、、失失用认、、失
认、视空间技能损害、执行功能障碍
视空间技能以损及害人格、和执行行为功改能变障等碍全面以性及痴人呆格表
Classification of Alzheimer’s Disease Using fMRI Data and Deep Learning Convolutional Neural Networks In this paper, we used convolutional neural network to classify Alzheimer’s brain from normal healthy brain. of clinical data such as Alzheimer’s disease has been always challenging and most problematic part has been always selecting the most discriminative features. Using Convolutional Neural Network (CNN) and the famous architecture LeNet5, we successfully classified functional MRI data of Alzheimer’s subjects from normal controls where the accuracy of test data on trained data reached 96.85%.
域包含了大量的联系纤维(内嗅区、海马、 海马旁回等与大脑额、颞叶的纤维联系)， 早期的病理改变常局限在此区域 • 线性测量的重复性和特异性较低
1. AD诊断-预处理
• 面积测量
• 测量前后连合间层面的额 叶、颞叶、侧脑室体部断 面、颞角和外侧裂平均横 断面面积
• 萎缩率最高的是颞角，然 后依次为侧脑室体部、外 侧裂、颞叶，最后为额叶
和行为改变现等为全特征面，性病痴因呆迄表今现未为明。特征，病
因迄今未明；
C
阿尔茨海默病尚未找到有效的治疗手段， 临床上”早期发现，早期干预”，对于减轻
病人脑部损害有非常重要的意义。
Classification of Alzheimer’s Disease Structural MRI Data by Deep Learning Convolutional Neural Networks
Saman Sarraf, Ghassem Tofighi, DeepAD: Alzheimer′s Disease Classification via Deep Convolutional Neural Networks using MRI and fMRI, /10.1101/070441
DeepAD: Alzheimer′s Disease Classification via Deep Convolutional Neural Networks using MRI and te-of-the-art deep learning-based pipelines employed to distinguish Alzheimer's magnetic resonance imaging (MRI) and functional MRI data from normal healthy control data for the same age group. Using these pipelines, which were executed on a GPU-based high performance computing platform, the data were strictly and carefully preprocessed. Next, scale and shift invariant low- to high-level features were obtained from a high volume of training images using convolutional neural network (CNN) architecture.
基于深度学习的医学影像大数据 分析
Zhao Di Computer Network Information Center
Chinese Academy of Sciences
2016年图形处理器技术大会 北京国际饭店会议中心，2016年9月13日
关于我
关于我：博士毕业论文
细胞间的热传导； 采用微分方程模型，全文上千公式；
深度学习——疾病的分类
• 脑：脑血管病，神经退行性疾病（阿尔茨海默病，帕金森病，癫 痫），精神疾病（抑郁症，精神分裂症），脑瘤
• 胸：心脏疾病，肺结节/肺癌，乳腺结节/乳腺癌 • 颈：颈动脉检测，甲状腺癌 • 眼：糖尿病眼病 • 皮肤：皮肤癌 • 腹部：胃癌 • 男性骨盆：前列腺癌 • 女性骨盆：子宫颈癌 •耳 •鼻 •背 • 四肢 •臀 •腰
Saman Sarraf, Ghassem Tofighi, Classification of Alzheimer's Disease using fMRI Data and Deep Learning Convolutional Neural Networks, arXiv:1603.08631 [cs.CV].18:58 2016/9/12
1. AD诊断
fMRI MRI PET
通 道 1
……
ROIs
通 道 N
多通道深度学习模型1
通 道 1
……
ROIs
通
道
N
……
多通道深度学习模型2通
道 1
ROIs
通 道 N
多通道深度学习模型3
……
……
CSF
其它临床诊断 深度学习模型N
……
权重w1 权重w2 权重w3 权重w(n-1) 权重w(n)
加权贝叶斯网络
阿尔茨海默病（Alzheimer's disease）