Pinned Repositories
2020-CBMS-DoubleU-Net
DoubleU-Net for Semantic Image Segmentation in TensorFlow & Pytorch (Nominated for Best Paper Award (IEEE CBMS))
2020-prenatal-sono
3D-GAN
brain tumor segmentation
morphsnakes
Morphological snakes for image segmentation and tracking
Multitasking
Multitasking UNet, Attention-UNet and Residual-Attention-UNet
nnUNet
OpenCV2-Python-Tutorials
This repo contains tutorials on OpenCV-Python library using new cv2 interface
python-codes-
shell-codes
UPES-MCA_DL
syedsajidhussain's Repositories
syedsajidhussain/AUCseg
A easy brain tumor segmentation method based clustering.
syedsajidhussain/Heart-Disease-Classifier-Web-App
Heart disease classifier web app
syedsajidhussain/two-stage-VAE-Attention-gate-BraTS2020
A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor Segmentation (BraTS 2020 Challenge; BrainLes2020 paper)
syedsajidhussain/DeepSeg
DeepSeg: Deep Neural Network Framework for Automatic Brain Tumor Segmentation using Magnetic Resonance FLAIR Images
syedsajidhussain/BRATS19-Multimodal-brain-tumor-segmentation
syedsajidhussain/Functionnectome
Project your functional brain signal onto the white matter, and explore the pathways supporting brain functions.
syedsajidhussain/Motor-Imagery-Papers
A list of papers for motor imagery using machine learning/deep learning.
syedsajidhussain/Tools-to-Design-or-Visualize-Architecture-of-Neural-Network
Tools to Design or Visualize Architecture of Neural Network
syedsajidhussain/Covid19_classification_tmempr
Medical images are crucial data sources for not easily diagnosed diseases. X-rays, one of the medical images, have high resolution. Processing high-resolution images leads to a few problems such as the difficulties in data storage, the computational load, and the time required to process high-dimensional data. It is a vital element to be able to diagnose diseases fast and accurately. In this study, a data set consisting of lung X-rays of patients with and without COVID-19 symptoms was taken into consideration and disease diagnosis from these images can be summarized in 2 steps as preprocessing and classification. Preprocessing step is the feature extraction process and in this step, the recently developed decomposition-based method Tridiagonal Matrix Enhanced Multivariance Products Representation (TMEMPR) is proposed as a feature extraction method. Classification of images is the second step where the Random Forest and Support Vector Machine (SVM) is applied as classifiers. Also, X-ray images have been reduced by 99,9\% with TMEMPR and with several state-of-the-art feature extraction methods which are Discrete Wavelet Transform (DWT), Discrete Cosine Transform(DCT) The results are examined under different feature extraction methods. It is observed that a higher accuracy rate of classification is achieved by using the TMEMPR method.
syedsajidhussain/Brain-Tumor-Classification
syedsajidhussain/SOTA-MedSeg
SOTA medical image segmentation methods based on various challenges
syedsajidhussain/MRtrix3_connectome
Generate subject connectomes from raw image data and perform inter-subject connection density normalisation, using tools provided in the MRtrix3 software package.
syedsajidhussain/MedicalZooPytorch
A pytorch-based deep learning framework for multi-modal 2D/3D medical image segmentation
syedsajidhussain/Brain-Tumor-Classification-1
Glioma Tumor Classification (MRI)
syedsajidhussain/TBSS
Tract based spatial statistics using ANTs and FSL
syedsajidhussain/youtube
Contains Notebook for my youtube channel videos
syedsajidhussain/SmoothAHAplot
Class for producing smooth 17 and 18 segment left ventricle plot, recommended by American Heart Association
syedsajidhussain/NeurIPs-2020-SlidesLive
Links to Presentations happened in NeurIPs 2020 via SlidesLive
syedsajidhussain/cnn-explainer
Learning Convolutional Neural Networks with Interactive Visualization.
syedsajidhussain/3D-MRI_Preprocessing
3D MRI preprocessing pipeline
syedsajidhussain/ml-class
Machine learning lessons and teaching projects designed for engineers
syedsajidhussain/hcp-diffusion-dcm
Scripts used in Experiment 2 of my PhD, using tractography and DCM on the HCP dataset
syedsajidhussain/pybrain_dipy
Two days dipy workshop
syedsajidhussain/geisinger-echo-mortality
Demo of Geisinger's trained models applied to Stanford Ouyangs Dataset
syedsajidhussain/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. ![](images/f1.png) 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. ![](images/f2.png) #### 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.
syedsajidhussain/BraTS20_Unet3d_AutoEncoder
3d unet and 3d autoencoder for automatical segmentation and feature extraction.
syedsajidhussain/streamlit-tensorflow-ml-app
Web App for Plant Disease Detection using Tensorflow and streamlit
syedsajidhussain/Transfer_Learning_Binary_Classification
I will implement the binary-class image classification using the VGG-16 Deep Convolutional Network as a Transfer Learning framework where the VGGNet comes pre-trained on the ImageNet dataset. For the experiment, we will use the Kaggle dogs-vs-cats dataset and classify the image objects into 2 classes. The classification accuracies of the VGG-16 model will be visualized using the confusion matrices.
syedsajidhussain/NMA
Repo for NeuroMatch Academy project on HCP dataset
syedsajidhussain/robust-glioma-segmentation
An implementation for "Robust Semantic Segmentation of Brain Tumor Regions from 3D MRIs."