Name: Mahmoud Mostapha
Type: User
Company: @NIRALUser
Bio: Mahmoud Mostapha is a Ph.D. student in the Department of Computer Science at the University of North Carolina at Chapel Hill (UNC), USA.
Location: University of North Carolina at Chapel Hill, Chapel Hill, NC
Blog: https://mahmoudmostapha.web.unc.edu/
Mahmoud Mostapha's Projects
COMP562 - Introduction to Machine Learning [Fall 2018]
Materials from deeplearning.ai course on Coursera
A simple and readable implementation of doc2vec, using Python 3, Keras and TensorFlow.
Slicer extensions index
ISBI 2018 Tutorial on CXR Anatomy Segmentation
Implementation of Adversarial Autoencoder in Keras
Multivariate Functional Shape Data Analysis (MFSDA) is a Matlab based package for statistical shape analysis. A multivariate varying coefficient model is introduced to build the association between the multivariate shape measurements and demographic information and other clinical variables. Statistical inference, i.e., hypothesis testing, is also included in this package, which can be used in investigating whether some covariates of interest are significantly associated with the shape information. The hypothesis testing results are further used in clustering based analysis, i.e., significant suregion detection. This MFSDA package is developed by Chao Huang and Hongtu Zhu from the BIG-S2 lab.
Multivariate Functional Shape Data Analysis in Python (MFSDA_Python) is a Python based package for statistical shape analysis. A multivariate varying coefficient model is introduced to build the association between the multivariate shape measurements and demographic information and other clinical, biological variables. Statistical inference, i.e., hypothesis testing, is also included in this package, which can be used in investigating whether some covariates of interest are significantly associated with the shape information. The hypothesis testing results are further used in clustering based analysis, i.e., significant suregion detection. This MFSDA package is developed by Chao Huang and Hongtu Zhu from the BIG-S2 lab.
Synthetic Experiment to Test MFSDA
Models and examples built with TensorFlow
Repository for the One class neural networks paper
Source Code for my PhD Dissertation
An archive of my Ph.D. Dissertation
Multivariate Functional Shape Data Analysis in Python (MFSDA_Python) is a Python based package for statistical shape analysis. A multivariate varying coefficient model is introduced to build the association between the multivariate shape measurements and demographic information and other clinical, biological variables.
Shape analysis has become of increasing interest to the medical community due to its potential to precisely locate morphological changes between healthy and pathological structures. SPHARM-PDM is a tool that computes point-based models using a parametric boundary description for the computing of Shape analysis.
TensorFlow Tutorials with YouTube Videos
Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.
Uniform Manifold Approximation and Projection
Keras implementation of the paper "Autoencoding beyond pixels using a learned similarity metric"