Aderghal2017

Title: Classification of sMRI for AD Diagnosis with Convolutional Neuronal Networks: A Pilot 2D+ \(\epsilon\) Study on ADNI

• (Aderghal, Benois-Pineau, and Afdel 2017)

The methods of Content-Based visual information indexing and retrieval penetrate into Healthcare and become popular in Computer-Aided Diagnostics. The PhD research we have started 13 months ago is devoted to the multimodal classification of MRI brain scans for Alzheimer Disease diagnostics. We use the winner classifier, such as CNN. We first proposed an original 2D+ approach. It avoids heavy volumetric computations and uses domain knowledge on Alzheimer biomarkers. We study discriminative power of different brain projections. Three binary classification tasks are considered separating Alzheimer Disease (AD) patients from Mild Cognitive Impairment (MCI) and Normal Control subject (NC). Two fusion methods on FC layer and on the single-projection CNN output show better performances, up to 91% of accuracy is achieved. The results are competitive with the SOA which uses heavier algorithmic chain.

• Training Method

  • 2 class classification (AD/CN, MCI/CN, AD/MCI)
  • Extract patches from hippocampus ROI
  • Data Augmentation (flipping, affine transform, blurring)
  • Balancing data (reduce over-sampled category, random duplicate under-sampled category, reduce augmented data)

• Result

Lin2018

Title: Convolutional neural networks-Based MRI image analysis for the Alzheimer’s disease prediction from mild cognitive impairment

• (Lin et al. 2018)

Mild cognitive impairment (MCI) is the prodromal stage of Alzheimer’s disease (AD). Identifying MCI subjects who are at high risk of converting to AD is crucial for effective treatments. In this study, a deep learning approach based on convolutional neural networks (CNN), is designed to accurately predict MCI-to-AD conversion with magnetic resonance imaging (MRI) data. First, MRI images are prepared with age-correction and other processing. Second, local patches, which are assembled into 2.5 dimensions, are extracted from these images. Then, the patches from AD and normal controls (NC) are used to train a CNN to identify deep learning features of MCI subjects. After that, structural brain image features are mined with FreeSurfer to assist CNN. Finally, both types of features are fed into an extreme learning machine classifier to predict the AD conversion. The proposed approach is validated on the standardized MRI datasets from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) project. This approach achieves an accuracy of 79.9% and an area under the receiver operating characteristic curve (AUC) of 86.1% in leave-one-out cross validations. Compared with other state-of-the-art methods, the proposed one outperforms others with higher accuracy and AUC, while keeping a good balance between the sensitivity and specificity. Results demonstrate great potentials of the proposed CNN-based approach for the prediction of MCI-to-AD conversion with solely MRI data. Age correction and assisted structural brain image features can boost the prediction performance of CNN.

• Training Method

  • 2 class classification (AD/CN)
  • Extract 2.5D patches for CNN training
  • Obtain the CNN-based and FreeSurfer-based image features
  • Select each features by PCA and Lasso
  • Combine two features and feed to extreme learning machine classifier

Aderghal2018

Title: Classification of Alzheimer disease on imaging modalities with deep CNNs using cross-modal transfer learning

• (Aderghal et al. 2018)(https://ieeexplore.ieee.org/document/8417262)

A recent imaging modality Diffusion Tensor Imaging completes information used from Structural MRI in studies of Alzheimer disease. A large number of recent studies has explored pathologic staging of Alzheimer disease using the Mean Diffusivity maps extracted from the Diffusion Tensor Imaging modality. The Deep Neural Networks are seducing tools for classification of subjects’ imaging data in computer-aided diagnosis of Alzheimer’s disease. The major problem here is the lack of a publicly available large amount of training data in both modalities. The lack number of training data yields over-fitting phenomena. We propose a method of a cross-modal transfer learning: from Structural MRI to Diffusion Tensor Imaging modality. Models pre-trained on a structural MRI dataset with domain-depended data augmentation are used as initialization of network parameters to train on Mean Diffusivity data. The method shows a reduction of the over-fitting phenomena, improves learning performance, and thus increases the accuracy of prediction. Classifiers are then fused by a majority vote resulting in augmented scores of classification between Normal Control, Alzheimer Patients and Mild Cognitive Impairment subjects on a subset of ADNI dataset.

• Training Method

  • 2 class classification (AD/CN, MCI/CN, AD/MCI)
  • Extract patches from hippocampus ROI
  • Data Augmentation (flip, zoom, shift, scale, contrast, noise …)
  • Fine-tuning pre-trained networks from MRI

• Result

Farooq2017

Title: A deep CNN based multi-class classification of Alzheimer’s disease using MRI

• (Farooq et al. 2017)(https://ieeexplore.ieee.org/document/8261460/)

In the recent years, deep learning has gained huge fame in solving problems from various fields including medical image analysis. This work proposes a deep convolutional neural network based pipeline for the diagnosis of Alzheimer’s disease and its stages using magnetic resonance imaging (MRI) scans. Alzheimer’s disease causes permanent damage to the brain cells associated with memory and thinking skills. The diagnosis of Alzheimer’s in elderly people is quite difficult and requires a highly discriminative feature representation for classification due to similar brain patterns and pixel intensities. Deep learning techniques are capable of learning such representations from data. In this paper, a 4-way classifier is implemented to classify Alzheimer’s (AD), mild cognitive impairment (MCI), late mild cognitive impairment (LMCI) and healthy persons. Experiments are performed using ADNI dataset on a high performance graphical processing unit based system and new state-of-the-art results are obtained for multiclass classification of the disease. The proposed technique results in a prediction accuracy of 98.8%, which is a noticeable increase in accuracy as compared to the previous studies and clearly reveals the effectiveness of the proposed method.

• Training Method

  • 4 class classification (AD/LMCI/EMCI/CN)
  • GoogLeNet, ResNet-18, ResNet-152

• Result

Wu2018

Title: Discrimination and conversion prediction of mild cognitive impairment using convolutional neural networks

• (Wu et al. 2018)(https://www.ncbi.nlm.nih.gov/pubmed/30598877)

Recently, studies have demonstrated that machine learning techniques, particularly cutting-edge deep learning technology, have achieved significant progression on the classification of Alzheimer’s disease (AD) and its prodromal phase, mild cognitive impairment (MCI). Moreover, accurate prediction of the progress and the conversion risk from MCI to probable AD has been of great importance in clinical application. In this study, the baseline MR images and follow-up information during 3 years of 150 normal controls (NC), 150 patients with stable MCI (sMCI) and 157 converted MCI (cMCI) were collected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The deep convolutional neural networks (CNNs) were adopted to distinguish different stages of MCI from the NC group, and predict the conversion time from MCI to AD. Two CNN architectures including GoogleNet and CaffeNet were explored and evaluated in multiple classifications and estimations of conversion risk using transfer learning from pre-trained ImageNet (via fine-tuning) and five-fold cross-validation. A novel data augmentation approach using random views aggregation was applied to generate abundant image patches from the original MR scans.

• Training Method

  • 3 class classification (sMCI/pMCI/CN)
  • Data Augmentation (combine 3 class slice into RGB color)
  • Fine-tuning pre-trained CaffeNet, GoogleNet

• Result

The GoogleNet acquired accuracies with 97.58%, 67.33% and 84.71% in three-way discrimination among the NC, sMCI and cMCI groups respectively, whereas the CaffeNet obtained promising accuracies of 98.71%, 72.04% and 92.35% in the NC, sMCI and cMCI classifications. Furthermore, the accuracy measures of conversion risk of patients with cMCI ranged from 71.25% to 83.25% in different time points using GoogleNet, whereas the CaffeNet achieved remarkable accuracy measures from 95.42% to 97.01% in conversion risk prediction. CONCLUSIONS: The experimental results demonstrated that the proposed methods had prominent capability in classification among the 3 groups such as sMCI, cMCI and NC, and exhibited significant ability in conversion risk prediction of patients with MCI.

Wang2018

Title: Classification of Alzheimer’s disease based on eight-layer convolutional neural network with leaky rectified linear unit and max pooling

• (Wang et al. 2018)(https://link.springer.com/article/10.1007/s10916-018-0932-7)

Alzheimer’s disease (AD) is a progressive brain disease. The goal of this study is to provide a new computer-vision based technique to detect it in an efficient way. The brain-imaging data of 98 AD patients and 98 healthy controls was collected using data augmentation method. Then, convolutional neural network (CNN) was used, CNN is the most successful tool in deep learning. An 8-layer CNN was created with optimal structure obtained by experiences. Three activation functions (AFs): sigmoid, rectified linear unit (ReLU), and leaky ReLU. The three pooling-functions were also tested: average pooling, max pooling, and stochastic pooling. The numerical experiments demonstrated that leaky ReLU and max pooling gave the greatest result in terms of performance. It achieved a sensitivity of 97.96%, a specificity of 97.35%, and an accuracy of 97.65%, respectively. In addition, the proposed approach was compared with eight state-of-the-art approaches. The method increased the classification accuracy by approximately 5% compared to state-of-the-art methods.

• Training Method

  • 2 class classification (AD/CN)
  • Data Augmentation (rotate, noise injection, random translation, scaling, affine transform)
  • Custom simple ConvNet (Conv-activation-Pool-Conv-activation: 8 layer)

• Result

Qiu2018

Title: Fusion of deep learning models of MRI scans, Mini–Mental State Examination, and logical memory test enhances diagnosis of mild cognitive impairment (2018)

• (Qiu et al. 2018)(https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6240705/)

Our aim was to investigate if the accuracy of diagnosing mild cognitive impairment (MCI) using the Mini-Mental State Examination (MMSE) and logical memory (LM) test could be enhanced by adding MRI data. METHODS: Data of individuals with normal cognition and MCI were obtained from the National Alzheimer Coordinating Center database (n=386). Deep learning models trained on MRI slices were combined to generate a fused MRI model using different voting techniques to predict normal cognition versus MCI. Two multilayer perceptron (MLP) models were developed with MMSE and LM test results. Finally, the fused MRI model and the MLP models were combined using majority voting. RESULTS: The fusion model was superior to the individual models alone and achieved an overall accuracy of 90.9%. DISCUSSION: This study is a proof of principle that multimodal fusion of models developed using MRI scans, MMSE, and LM test data is feasible and can better predict MCI.

• Training Method

  • using MRI
  • using Mini–Mental State Examination(MMSE) result
  • usingWechsler Memory Scale Logical memory(LM) test result
  • 2 class classification (sMCI/pMCI)
  • Ensemble three VGG-11 models using majority voting
  • Ensemble three model (ensembled VGG-11, MMSE model, LM model)

• Result

Cheng2017

Title: Classification of MR brain images by combination of multi-CNNs for AD diagnosis

• Cheng2017

Alzheimer’s disease (AD) is an irreversible neurodegenerative disorder with progressive impairment of memory and cognitive functions. Its early diagnosis is crucial for development of future treatment. Magnetic resonance images (MRI) play important role to help understand the brain anatomical changes related to AD. Conventional methods extract the hand-crafted features such as gray matter volumes and cortical thickness and train a classifier to distinguish AD from other groups. Different from these methods, this paper proposes to construct multiple deep 3D convolutional neural networks (3D-CNNs) to learn the various features from local brain images which are combined to make the final classification for AD diagnosis. First, a number of local image patches are extracted from the whole brain image and a 3D-CNN is built upon each local patch to transform the local image into more compact high-level features. Then, the upper convolution and fully connected layers are fine-tuned to combine the multiple 3D-CNNs for image classification. The proposed method can automatically learn the generic features from imaging data for classification. Our method is evaluated using T1-weighted structural MR brain images on 428 subjects including 199 AD patients and 229 normal controls (NC) from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Experimental results show that the proposed method achieves an accuracy of 87.15% and an AUC (area under the ROC curve) of 92.26% for AD classification, demonstrating the promising classification performances.

• Training Method

  • 2 class classification (AD/CN)
  • Extract multiple local patches
  • Pre-training each CNN
  • Combine each CNN with Fully Connected(FC) layer and fine-tuning

• Result

Liu2018

Title: Multi-modality cascaded convolutional neural networks for Alzheimer’s disease diagnosis

• (Liu et al. 2018)

Accurate and early diagnosis of Alzheimer’s disease (AD) plays important role for patient care and development of future treatment. Structural and functional neuroimages, such as magnetic resonance images (MRI) and positron emission tomography (PET), are providing powerful imaging modalities to help understand the anatomical and functional neural changes related to AD. In recent years, machine learning methods have been widely studied on analysis of multi-modality neuroimages for quantitative evaluation and computer-aided-diagnosis (CAD) of AD. Most existing methods extract the hand-craft imaging features after image preprocessing such as registration and segmentation, and then train a classifier to distinguish AD subjects from other groups. This paper proposes to construct cascaded convolutional neural networks (CNNs) to learn the multi-level and multimodal features of MRI and PET brain images for AD classification. First, multiple deep 3D-CNNs are constructed on different local image patches to transform the local brain image into more compact high-level features. Then, an upper high-level 2D-CNN followed by softmax layer is cascaded to ensemble the high-level features learned from the multi-modality and generate the latent multimodal correlation features of the corresponding image patches for classification task. Finally, these learned features are combined by a fully connected layer followed by softmax layer for AD classification. The proposed method can automatically learn the generic multi-level and multimodal features from multiple imaging modalities for classification, which are robust to the scale and rotation variations to some extent. No image segmentation and rigid registration are required in pre-processing the brain images. Our method is evaluated on the baseline MRI and PET images of 397 subjects including 93 AD patients, 204 mild cognitive impairment (MCI, 76 pMCI +128 sMCI) and 100 normal controls (NC) from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Experimental results show that the proposed method achieves an accuracy of 93.26% for classification of AD vs. NC and 82.95% for classification pMCI vs. NC, demonstrating the promising classification performance.

• Training Method

  • 2 class classification (AD/CN, pMCI/CN, sMCI/CN)
  • Divide into 3x3x3 size from whole brain image –> extract 27 patches
  • Extract feature with 3D ConvNet
  • Combined feature vector of MRI and PE

• Result

Li2018

Title: Alzheimer’s disease diagnosis based on multiple cluster dense convolutional networks

• (Li and Liu 2018)

Alzheimer’s disease (AD) is an irreversible neurodegenerative disorder with progressive impairment of memory and cognitive functions. Structural magnetic resonance images (MRI) play important role to evaluate the brain anatomical changes for AD Diagnosis. Machine learning technologies have been widely studied on MRI computation and analysis for quantitative evaluation and computer-aided-diagnosis of AD. Most existing methods extract the hand-craft features after image processing such as registration and segmentation, and then train a classifier to distinguish AD subjects from other groups. Motivated by the success of deep learning in image classification, this paper proposes a classification method based on multiple cluster dense convolutional neural networks (DenseNets) to learn the various local features of MR brain images, which are combined for AD classification. First, we partition the whole brain image into different local regions and extract a number of 3D patches from each region. Second, the patches from each region are grouped into different clusters with the K-Means clustering method. Third, we construct a DenseNet to learn the patch features for each cluster and the features learned from the discriminative clusters of each region are ensembled for classification. Finally, the classification results from different local regions are combined to enhance final image classification. The proposed method can gradually learn the MRI features from the local patches to global image level for the classification task. There are no rigid registration and segmentation required for preprocessing MRI images. Our method is evaluated using T1-weighted MRIs of 831 subjects including 199 AD patients, 403 mild cognitive impairment (MCI) and 229 normal control (NC) subjects from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Experimental results show that the proposed method achieves an accuracy of 89.5% and an AUC (area under the ROC curve) of 92.4% for AD vs. NC classification, and an accuracy of 73.8% and an AUC of 77.5% for MCI vs. NC classification, demonstrating the promising classification performances.

• Training Method

  • 2 class classification (AD/CN, MCI/CN)
  • Find the groups of patches by K-means clustering (32x32x32 size)
  • Reduce feature dimension by PCA (32x32x32 –> 2000 dim)
  • Fine-tuning pre-trained 3D DenseNet

• Result

Lian2020

Title: Hierarchical Fully Convolutional Network for Joint Atrophy Localization and Alzheimer’s Disease Diagnosis using Structural MRI

• (Lian et al. 2020)

Structural magnetic resonance imaging (sMRI) has been widely used for computer-aided diagnosis of neurodegenerative disorders, e.g., Alzheimer’s disease (AD), due to its sensitivity to morphological changes caused by brain atrophy. Recently, a few deep learning methods (e.g., convolutional neural networks, CNNs) have been proposed to learn task-oriented features from sMRI for AD diagnosis, and achieved superior performance than the conventional learning-based methods using hand-crafted features. However, these existing CNN-based methods still require the pre-determination of informative locations in sMRI. That is, the stage of discriminative atrophy localization is isolated to the latter stages of feature extraction and classifier construction. In this paper, we propose a hierarchical fully convolutional network (H-FCN) to automatically identify discriminative local patches and regions in the whole brain sMRI, upon which multi-scale feature representations are then jointly learned and fused to construct hierarchical classification models for AD diagnosis. Our proposed H-FCN method was evaluated on a large cohort of subjects from two independent datasets (i.e., ADNI-1 and ADNI-2), demonstrating good performance on joint discriminative atrophy localization and brain disease diagnosis.

• Training Method

  • 2 class classification (AD/CN, pMCI/sMCI)
  • Data Augmentation (random flipping, ditorting, shifting)
  • Process each patch by patch-level sub-networks (shared parameter) and concat each ouput feature
  • Repeat feature encoding by region-level and subject-level networks
  • Delete uninformative sub-networks (Network Pruning)

• Result

Vu2017

Title: Multimodal learning using convolution neural network and Sparse Autoencoder

• (Vu et al. 2017)

In the last decade, pattern recognition methods using neuroimaging data for the diagnosis of Alzheimer’s disease (AD) have been the subject of extensive research. Deep learning has recently been a great interest in AD classification. Previous works had done almost on single modality dataset, such as Magnetic Resonance Imaging (MRI) or Positron Emission Tomography (PET), shown high performances. However, identifying the distinctions between Alzheimer’s brain data and healthy brain data in older adults (age > 75) is challenging due to highly similar brain patterns and image intensities. The corporation of multimodalities can solve this issue since it discovers and uses the further complementary of hidden biomarkers from other modalities instead of only one, which itself cannot provide. We therefore propose a deep learning method on fusion multimodalities. In details, our approach includes Sparse Autoencoder (SAE) and convolution neural network (CNN) train and test on combined PET-MRI data to diagnose the disease status of a patient. We focus on advantages of multimodalities to help providing complementary information than only one, lead to improve classification accuracy. We conducted experiments in a dataset of 1272 scans from ADNI study, the proposed method can achieve a classification accuracy of 90% between AD patients and healthy controls, demonstrate the improvement than using only one modality.

• Training Method

  • 2 class classification (AD/CN, MCI/CN)
  • Extract 3D patches from MRI, PET
  • Pre-training each input feauture by sparse autoencoder
  • Learn by parameter shared CNN

• Result

Shmulev2018

Title: Predicting Conversion of Mild Cognitive Impairments to Alzheimer’s Disease and Exploring Impact of Neuroimaging

• (Shmulev and Belyaev 2018)

Nowadays, a lot of scientific efforts are concentrated on the diagnosis of Alzheimer’s Disease (AD) applying deep learning methods to neuroimaging data. Even for 2017, there were published more than a hundred papers dedicated to AD diagnosis, whereas only a few works considered a problem of mild cognitive impairments (MCI) conversion to the AD. However, the conversion prediction is an important problem since approximately 15% of patients with MCI converges to the AD every year. In the current work, we are focusing on the conversion prediction using brain Magnetic Resonance Imaging and clinical data, such as demographics, cognitive assessments, genetic, and biochemical markers. First of all, we applied state-of-the-art deep learning algorithms on the neuroimaging data and compared these results with two machine learning algorithms that we fit using the clinical data. As a result, the models trained on the clinical data outperform the deep learning algorithms applied to the MR images. To explore the impact of neuroimaging further, we trained a deep feed-forward embedding using similarity learning with Histogram loss on all available MRIs and obtained 64-dimensional vector representation of neuroimaging data. The use of learned representation from the deep embedding allowed to increase the quality of prediction based on the neuroimaging. Finally, the current results on this dataset show that the neuroimaging does affect conversion prediction, however, cannot noticeably increase the quality of the prediction. The best results of predicting MCI-to-AD conversion are provided by XGBoost algorithm trained on the clinical and embedding data. The resulting accuracy is 0.76 +- 0.01 and the area under the ROC curve - 0.86 +- 0.01.

• Training Method

  • MRI, clinical dataset(demographics, cognitive test data, biospecimen data)
  • 2 class classification (sMCI/pMCI)
  • Feature extract from ResNet3D + Histogram loss

• Result