Early Diagnosis of Alzheimer’s Disease using Deep Learning and Ensemble Learning Research

本篇文章为 Early Diagnosis of Alzheimer’s Disease using Deep Learning and Ensemble Learning 的 research 日志。

Week 1

考虑个人没有生物学基础，所以要先从 Alzheimer Disease (后文简称 AD) 本身开始了解。

通过阅读以下文章了解 AD 的诊断过程并做出总结：

• Alzheimer’s Disease facts and figures
• Alzheimer's Disease: How It’s Diagnosed
• Early Detection of Alzheimer’s Disease Using Magnetic Resonance Imaging: A Novel Approach Combining Convolutional Neural Networks and Ensemble Learning
• Wikipedia 上的 AD 相关内容

初步发现关于诊断 AD 最困难的问题，本质在于并不知道该病的根源，医生无法通过从一个固定的方向进行诊断。

具体总结在 AD background reading

那么对于这种情况，我们可以通过较大的数据集分析出病状的 common feature 去找到 AD 患者的常见症状以及身体状况。

此时便可以用到 deep learning 和 ensemble learning。

为了建立对于该项目的初步了解，我阅读了 A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain

该篇文章针对 75.8 个月的患者数据集，采集 18F-FDG PET brain images，通过 deep learning model 发现该放射性 元素关于 AD 的 Specificity 和 Sensitvity。

鉴于对专业术语和背景知识的不够了解，对本篇文章的解读并不够完善，可能还需要进一步学习。

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Week 2

根据阅读之前那篇文章不够理解的部分进行学习

通过阅读 Evaluating Categorical Models II: Sensitivity and Specificity 我们学习到：

• Sensitivity

  • the metric that evaluates a model’s ability to predict true positives of each available category
    • $ = \frac{\texttt{True Positives}}{\texttt{True Positives + False Negatives}}$

• Specificity

  • the metric that evaluates a model’s ability to predict true negatives of each available category.
    • = \(\frac{\texttt{True Negatives}}{\texttt{True Negatives + False Positives}}\)

在之前那篇文章中还有 F1 Score 和 ROC curve 的概念：

• F1 Score (F-score, F-measure)

  • measure of a test's accuracy
  • Precision: True Positive divided by all positive results
    • \(=\frac{\texttt{True Positives}}{\texttt{True Positives+False Positives}}\)
  • Recall: True Positive divided by results that should have been postive
    • \(=\frac{\texttt{True Positives}}{\texttt{True Positives+False Positives}}\)
  • \(F_1\) Score is calculated from the precision and recall of the test by the equation:
    • \(F_1 = \frac{2}{\texttt{recall}^{-1}+\texttt{precision}^{-1}}=2\times \frac{\texttt{precision}\times\texttt{recall}}{\texttt{precision}+\texttt{recall}}=\frac{\text{tp}}{\text{tp}+\frac{1}{2}\text{(fp+fn)}}\)
  • \(F_\beta\) Score is a more general F score, \(\beta\) is chosen such that recall is considered \(\beta\) times as important as precision.
    • \(F_\beta=(1+\beta)^2\times\frac{\texttt{precision}\times\texttt{recall}}{(\beta^2\times\texttt{precision})+\texttt{recall}}\)

• ROC curve (receiver operating characteristic curve)

  • The curve plots two parameters: True Positive Rate & False Positive Rate
  • TPR
    • \(=\frac{\text{TP}}{\text{TP+FN}}\)
  • FPR
    • \(=\frac{\text{FP}}{\text{FP+TN}}\)
  • A ROC curve plots TPR vs. FPR at different classification thresholds. If a point is more close to the top left corner, it has better prediction.

• AUC (Area under the Roc Curve)

  • AUC provides an aggregate measure of performance across all possible classification thresholds. One way of interpreting AUC is as the probability that the model ranks a random positive example more highly than a random negative example.

• Transfer Learning

  • Generally, TL is to store knowledge gained while solving one problem and apply it to another related problem.
    • Reusing data and transfering information from previously learned tasks for the learning of new tasks help improve the sample efficiency of a reinforcement learning agent.

有一个 machine learning algorithms 相关的 python library 叫做 Scikit-Learn。

如果日后要做相关编程及研究，对该 package 的使用至关重要。

Tutorial: Introducing-Scikit-Learn.ipynb