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himanshub1007 / Alzhimers Disease Prediction Using Deep Learning# AD-Prediction Convolutional Neural Networks for Alzheimer's Disease Prediction Using Brain MRI Image ## Abstract Alzheimers disease (AD) is characterized by severe memory loss and cognitive impairment. It associates with significant brain structure changes, which can be measured by magnetic resonance imaging (MRI) scan. The observable preclinical structure changes provides an opportunity for AD early detection using image classification tools, like convolutional neural network (CNN). However, currently most AD related studies were limited by sample size. Finding an efficient way to train image classifier on limited data is critical. In our project, we explored different transfer-learning methods based on CNN for AD prediction brain structure MRI image. We find that both pretrained 2D AlexNet with 2D-representation method and simple neural network with pretrained 3D autoencoder improved the prediction performance comparing to a deep CNN trained from scratch. The pretrained 2D AlexNet performed even better (**86%**) than the 3D CNN with autoencoder (**77%**). ## Method #### 1. Data In this project, we used public brain MRI data from **Alzheimers Disease Neuroimaging Initiative (ADNI)** Study. ADNI is an ongoing, multicenter cohort study, started from 2004. It focuses on understanding the diagnostic and predictive value of Alzheimers disease specific biomarkers. The ADNI study has three phases: ADNI1, ADNI-GO, and ADNI2. Both ADNI1 and ADNI2 recruited new AD patients and normal control as research participants. Our data included a total of 686 structure MRI scans from both ADNI1 and ADNI2 phases, with 310 AD cases and 376 normal controls. We randomly derived the total sample into training dataset (n = 519), validation dataset (n = 100), and testing dataset (n = 67). #### 2. Image preprocessing Image preprocessing were conducted using Statistical Parametric Mapping (SPM) software, version 12. The original MRI scans were first skull-stripped and segmented using segmentation algorithm based on 6-tissue probability mapping and then normalized to the International Consortium for Brain Mapping template of European brains using affine registration. Other configuration includes: bias, noise, and global intensity normalization. The standard preprocessing process output 3D image files with an uniform size of 121x145x121. Skull-stripping and normalization ensured the comparability between images by transforming the original brain image into a standard image space, so that same brain substructures can be aligned at same image coordinates for different participants. Diluted or enhanced intensity was used to compensate the structure changes. the In our project, we used both whole brain (including both grey matter and white matter) and grey matter only. #### 3. AlexNet and Transfer Learning Convolutional Neural Networks (CNN) are very similar to ordinary Neural Networks. A CNN consists of an input and an output layer, as well as multiple hidden layers. The hidden layers are either convolutional, pooling or fully connected. ConvNet architectures make the explicit assumption that the inputs are images, which allows us to encode certain properties into the architecture. These then make the forward function more efficient to implement and vastly reduce the amount of parameters in the network. #### 3.1. AlexNet The net contains eight layers with weights; the first five are convolutional and the remaining three are fully connected. The overall architecture is shown in Figure 1. The output of the last fully-connected layer is fed to a 1000-way softmax which produces a distribution over the 1000 class labels. AlexNet maximizes the multinomial logistic regression objective, which is equivalent to maximizing the average across training cases of the log-probability of the correct label under the prediction distribution. The kernels of the second, fourth, and fifth convolutional layers are connected only to those kernel maps in the previous layer which reside on the same GPU (as shown in Figure1). The kernels of the third convolutional layer are connected to all kernel maps in the second layer. The neurons in the fully connected layers are connected to all neurons in the previous layer. Response-normalization layers follow the first and second convolutional layers. Max-pooling layers follow both response-normalization layers as well as the fifth convolutional layer. The ReLU non-linearity is applied to the output of every convolutional and fully-connected layer.  The first convolutional layer filters the 224x224x3 input image with 96 kernels of size 11x11x3 with a stride of 4 pixels (this is the distance between the receptive field centers of neighboring neurons in a kernel map). The second convolutional layer takes as input the (response-normalized and pooled) output of the first convolutional layer and filters it with 256 kernels of size 5x5x48. The third, fourth, and fifth convolutional layers are connected to one another without any intervening pooling or normalization layers. The third convolutional layer has 384 kernels of size 3x3x256 connected to the (normalized, pooled) outputs of the second convolutional layer. The fourth convolutional layer has 384 kernels of size 3x3x192 , and the fifth convolutional layer has 256 kernels of size 3x3x192. The fully-connected layers have 4096 neurons each. #### 3.2. Transfer Learning Training an entire Convolutional Network from scratch (with random initialization) is impractical[14] because it is relatively rare to have a dataset of sufficient size. An alternative is to pretrain a Conv-Net on a very large dataset (e.g. ImageNet), and then use the ConvNet either as an initialization or a fixed feature extractor for the task of interest. Typically, there are three major transfer learning scenarios: **ConvNet as fixed feature extractor:** We can take a ConvNet pretrained on ImageNet, and remove the last fully-connected layer, then treat the rest structure as a fixed feature extractor for the target dataset. In AlexNet, this would be a 4096-D vector. Usually, we call these features as CNN codes. Once we get these features, we can train a linear classifier (e.g. linear SVM or Softmax classifier) for our target dataset. **Fine-tuning the ConvNet:** Another idea is not only replace the last fully-connected layer in the classifier, but to also fine-tune the parameters of the pretrained network. Due to overfitting concerns, we can only fine-tune some higher-level part of the network. This suggestion is motivated by the observation that earlier features in a ConvNet contains more generic features (e.g. edge detectors or color blob detectors) that can be useful for many kind of tasks. But the later layer of the network becomes progressively more specific to the details of the classes contained in the original dataset. **Pretrained models:** The released pretrained model is usually the final ConvNet checkpoint. So it is common to see people use the network for fine-tuning. #### 4. 3D Autoencoder and Convolutional Neural Network We take a two-stage approach where we first train a 3D sparse autoencoder to learn filters for convolution operations, and then build a convolutional neural network whose first layer uses the filters learned with the autoencoder.  #### 4.1. Sparse Autoencoder An autoencoder is a 3-layer neural network that is used to extract features from an input such as an image. Sparse representations can provide a simple interpretation of the input data in terms of a small number of \parts by extracting the structure hidden in the data. The autoencoder has an input layer, a hidden layer and an output layer, and the input and output layers have same number of units, while the hidden layer contains more units for a sparse and overcomplete representation. The encoder function maps input x to representation h, and the decoder function maps the representation h to the output x. In our problem, we extract 3D patches from scans as the input to the network. The decoder function aims to reconstruct the input form the hidden representation h. #### 4.2. 3D Convolutional Neural Network Training the 3D convolutional neural network(CNN) is the second stage. The CNN we use in this project has one convolutional layer, one pooling layer, two linear layers, and finally a log softmax layer. After training the sparse autoencoder, we take the weights and biases of the encoder from trained model, and use them a 3D filter of a 3D convolutional layer of the 1-layer convolutional neural network. Figure 2 shows the architecture of the network. #### 5. Tools In this project, we used Nibabel for MRI image processing and PyTorch Neural Networks implementation.
OHDSI / CohortMethodAn R package for performing new-user cohort studies in an observational database in the OMOP Common Data Model.
vishnubashyam / DeepBrainNetConvolutional Neural Network trained for age prediction using a large (n=11,729) set of MRI scans from a highly diversified cohort spanning different studies, scanners, ages, ethnicities and geographic locations around the world.
aphp / Cohort360 FrontEndA web application to find patients, build cohorts and visualize health records
Warvito / Normative Modelling Using Deep AutoencodersNormative modelling using deep autoencoders: a multi-cohort study on mild cognitive impairment and Alzheimer’s disease
MK-SWE / ALX Cohort 18 StudyALX Cohort 18 Study
Cassie07 / Review Molecular Profile Prediction GNN[The Lancet Digital Health, MICCAI2020 Oral] The official code of "Spatially-aware Graph Neural Networks and Cross-level Molecular Profile Prediction in Colon Cancer Histopathology: A Retrospective Multicentre Cohort Study"
anitalu724 / MutScapeＡ user-friendly Python toolkit, which provides a comprehensive pipeline to easily explore the cohort-based mutational characterization for studying cancer genomics.
Lean-IN-IGDTUW / WebDevelopmentStudy material, Resources & Tasks for Web Development cohort 2020 - 2021.
andreamazzella / R4asmeR material for LSHTM's Advanced Statistical Methods in Epidemiology (ASME) practical sessions
HaloForest / UKB PWASCode utilized in the paper "A phenome-wide association and Mendelian randomization study for Alzheimer's disease: a prospective cohort study of 502,493 participants from UK Biobank"
Fanyi177 / Effects Of Diets On Risks Of Cancer And The Mediating Role Of MetabolitesEffects of Adherence to Mediterranean and MIND Diets, and the Mediating Effect of Metabolites on Risks of Overall and 22 Specific Cancers: a cohort study in UK Biobank
episphere / ConnectConnect API for DCEG's Cohort Study
ABCD-STUDY / Abcd Acs Raked PropensityHow to calculate raked propensity scores for the ABCD Study cohort
david-a-parry / VaseVariant Annotation, Segregation and Exclusion for family or cohort based rare-disease sequencing studies.
Akatsuki-Coding-Club / Data Structures Algorithms Akatsuki Coding CLubWelcome to the Hacktoberfest DSA Cohort for R.C. Patel Institute of Technology students! To request issue assignment, create a pull request, providing: 1. Full Name 🧑🎓 2.Email 📧 3.College ID (RNO) 🔢 4.Branch of Study.📚 5. Year 📆 .R.C. Patel Institute of Technology students' PRs will be considered only.Thank you!
HAIRLAB / ECGSource code of "Automatic multilabel electrocardiogram diagnosis of heart rhythm or conduction abnormalities with deep learning: a cohort study"
NeuroADaS-Lab / Multilayer MRIIn this study, we aimed to combine the morphological, structural and functional information defining a new multilayer network perspective, which has been shown advantageous to jointly analyse multiple types of relational information regarding the same objects at once by using graph-mining techniques. The main contribution of this work is the design, development and validation of a multilayer scheme that combines these three layers of information into a unique multilayer network, where the integrity of white matter connections links and relates grey matter probability maps and resting-state fMRI. To validate our framework, we also extend several metrics from graph theory to adapt them to our specific domain characteristics. This proof of concept has been applied to a cohort of people with MS, it has shown that we are able to identify several brain regions with a synchronised connectivity deterioration.
GRONINGEN-MICROBIOME-CENTRE / Lifelines NEXTLifelines NEXT is a birth cohort designed to study the effects of intrinsic and extrinsic determinants on health and disease in a four-generation design. It is embedded within the Lifelines cohort study, a prospective three-generation population-based cohort study recording the health and health-related aspects of 167,729 individuals living in Northern Netherlands. In Lifelines NEXT we include 1500 pregnant women intensively follow them, their partners and their children until at least 1 year after birth. This repository describes the codes for the microbiome associated projects within Lifelines NEXT including fecal (microbiome and virome), breastmilk microbiome, vaginal mcirobiome and oral microbiome. Here we also integrate mutpliple multi-omic layers including metabolomics, proteomics and extensive phenotypic data
IsaacCheng9 / Machine Learning In ChessMy final year project for the University of Exeter, using machine learning to study patterns in millions of chess games (~350 GB). Ranked 1st in the cohort for undergraduate projects (85%).
