條件對比嵌入網路: 透過元資訊學習輔助磁振造影特徵向量的學習

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研究生：	謝宛庭 Hsieh, Wan-Ting
論文名稱：	條件對比嵌入網路: 透過元資訊學習輔助磁振造影特徵向量的學習 A Condition-Contrastive Embedding Network: Using Meta Information to Guide MRI Representation Learning
指導教授：	李祈均 Lee, Chi-Chun
口試委員:	彭旭霞 Peng, Hsu-Hsia 吳恩賜 Goh, Oon Soo Joshua 陳煥宗 Chen, Hwann-Tzong
學位類別：	碩士 Master
系所名稱：	電機資訊學院 - 電機工程學系 Department of Electrical Engineering
論文出版年：	2020
畢業學年度：	107
語文別：	英文
論文頁數：	52
中文關鍵詞：	功能性磁振造影、特徵向量學習、人臉識別能力、失智症
外文關鍵詞：	fMRI, representation learning, face perception ability, dementia
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近年來功能性核磁共振造影在理解人類行為的領域中成為一股潮流，神經科學方面的研究也顯示透過條件限制的腦活動可用來解釋人類的身心狀態或是行為的表現。同時隨著深度學習技術的發展，工程中開始利用磁振造影的特徵向量進行人類行為辨識以及疾病預測。然而，在學習特徵向量的過程中往往忽略了元資訊對磁振造影訊號所帶來的影響，換言之，神經科學所使用的限制條件(例如刺激物的設計或是行為量表結果)在磁振造影的訊號處理時被忽略了。為了解決這個問題，本研究提出條件對比嵌入網路以將這些額外卻重要的資訊融入在我們的架構中，一同學習更全面的特徵向量。透過此方法，我們在臉部識別能力辨識任務以及失智症辨識任務中都有較高的準確率，且透過視覺化、t-test、重要樞紐偵測等分析，我們發現對任務而言的重要腦區資訊也一同被學習在特徵向量中。

Functional Magnetic Resonance Imaging (fMRI) has recently become a trend for understanding human behavior. Researches from neuroscience have demonstrated that brain activation under certain condition shown in fMRI could explain the external behavior status and performance. With the advent of deep learning, computational works are conducted to fulfill the disease detection or behavior prediction using representation derived from fMRI. However, the previous works may ignore what meta information, i.e., stimuli manipulation and behavior performance, has brought into fMRI signal during the representation learning process. In this study, we propose a Condition-Contrastive Embedding Network (CCEN) to incorporate meta information into our architecture to learn a more comprehensive representation. We experiment this method on two tasks: Face Perception Ability Recognition and Dementia Classification Task, where we found better predicting performance using the representation learned from CCEN framework. Furthermore, through visualization, t-test and hub alteration detection analysis, we discover that the information of key brain regions is simultaneously embedded in our learned representation.

摘要                                                         I
ABSTRACT                                                    II
誌謝                                                        III
CONTENTS                                                    IV
CHAPTER 1 INTRODUCTION                                       1
CHAPTER 2 FACE PERCEPTION ABILITY RECOGNITION                5
1 INTRODUCTION                                             5
2 DATABASE                                                 8
2.1 fMRI Acquisition and Processing                        9
2.2 Face Perception and Memory Assessments                 11
2.2.1 Taiwanese Face and Memory Test (TFMT)                11
2.2.2 Component Task                                       11
3 RESEARCH METHODOLOGY                                     12
3.1 Graph Embedding Techniques                             13
3.2 Event-Contrastive Connectome Network (E-cCN)           15
3.2.1 Graph Construction                                   15
3.2.2 Graph Embedding of Functional Connectivity           15
3.2.3 Contrastive Loss Embedded Network                    16
4 EXPERIMENTS AND RESULTS                                  17
4.1 Experimental Settings                                  17
4.2 Network Configuration                                  18
4.3 Recognition Results                                    18
4.4 Brain Connectome Visualization                         21
CHAPTER 3 DEMENTIA CLASSIFICATION TASK                       23
1 INTRODUCTION                                             23
2 DATABASE                                                 27
2.1 Resting-State fMRI Acquisition and Processing          28
2.2 DTI Acquisition and Processing                         29
2.3 Neuropsychological Assessment                          30
2.3.1 CDR                                                  30
2.3.2 MMSE                                                 30
2.3.3 MoCA                                                 31
3 RESEARCH METHODOLOGY                                     31
3.1 Behavior Score-Embedded Encoder Network (BSEN)         31
3.1.1 The Convolutional Autoencoder (ModelAE)              32
3.1.2 The Contrastive Center Loss Model (ModelC)           32
3.1.3 Dementia Classification and Fusion Technique         33
4 EXPERIMENTS AND RESULTS                                  34
4.1 Experimental Settings                                  34
4.2 Network Configuration                                  35
4.3 Recognition Results of Rs-fMRI Database                36
4.4 Recognition Results of DTI Database                    38
4.5 Statistical Analysis of Rs-fMRI Database               41
4.6 Nodal Efficiency Analysis of DTI Database              43
CHAPTER 4    CONCLUSION                                      47
REFERENCE                                                    48


                                

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