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Alzheimer Prediction Review

01. Deep Structural and Clinical Feature Learning for Semi-Supervised Multiclass Prediction of Alzheimer’s Disease (2018)

https://sci-hub.tw/https://ieeexplore.ieee.org/abstract/document/8623946\

Dataset

  • volume, surface area, cortical thickness, cognitive test features.

Training

  • Sparse Autoencoder based feature learning
  • pretrained weight + logistic layer
    01_model

Result

  • SVM-based methods
- Deep learning methods

02. Deep learning reveals Alzheimer's disease onset in MCI subjects: Results from an international challenge (2018)

https://sci-hub.tw/https://www.sciencedirect.com/science/article/pii/S0165027017304296\

Dataset

Training

  • select features by Random Forest
  • DNN training

Result

03. Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer’s Disease (2018)

https://arxiv.org/pdf/1806.01738.pdf\

INPUT

  • use the volumes of all C = 138 segmented brain structures
  • +graph information (age, sex, genetic info)

TRAINING

  • Graph Convolutional networks
    03_model

RESULT

  • Influence of polynomial order
  • Influence of phenotypic measures

04. A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease (2019)

https://sci-hub.tw/https://www.sciencedirect.com/science/article/pii/S105381191930031X

INPUT

  • MRI, extracted local Jacobian Determinant(JD) image, tabular clinical data (Multimodal)

TRAINING

  • Feature extractor layer are shared two task
  • predict two tasks (AD/HC, pMCI/sMCI)

RESULT

  • ::

05. A deep learning model for early prediction of Alzheimer's disease dementia based on hippocampal MRI (2019)

https://arxiv.org/ftp/arxiv/papers/1904/1904.07282.pdf

INPUT

  • (left/right) Hippocampus extracted from T1 MRI

TRAINING

05_model

RESULT

  • map
    05_result

06. Hippocampus Analysis by Combination of 3D DenseNet and Shapes for Alzheimer’s Disease Diagnosis (2018)

https://sci-hub.tw/10.1109/jbhi.2018.2882392

INPUT

  • (left/right) Hippocampus segmentation and 3D patch extraction

TRAINING

  • overview
    06_model1
  • 3D DenseNet : learn visual features
    06_model2
  • Hippocampal Shape Analysis : extract shape features
    06_model3

RESULT

  • comparison of different patch size
    06_result1
  • comparison of different model
    06_result2
  • comparison of different methods
    06_result3

07. Alzheimer's disease diagnosis based on multiple cluster dense convolutional networks (2018)

https://sci-hub.tw/https://www.sciencedirect.com/science/article/abs/pii/S089561111830199X

INPUT

  • 3D patches from T1 MRI

TRAINING

  1. find the groups of patches by K-means clustering (32x32x32 size)
  2. reduce feature dimension by PCA (2000 dim)
  3. use Multi-clsuter DenseNet
    07_model

RESULT

  • comparison of different K
    07_result1
  • comparison of different model
    07_result2

Tabular data application

01. SuperTML: Two-Dimensional Word Embedding for the Precognition on Structured Tabular Data (2019)

https://arxiv.org/pdf/1903.06246v3.pdf\

TRAINING

  • images with two-dim embeddings of the features in tabular data _01_data1 _01_data2
  • fine-tuning the pretrained CNN model.

RESULT

_01_result1 _01_result2

02. Regularization Learning Networks: Deep Learning for Tabular Datasets (2018)

https://arxiv.org/pdf/1805.06440v3.pdf