Name: Mohamed Amgad Tageldin
Type: User
Company: Northwestern University
Bio: Pathology resident physician at Northwestern University, with an MD from Cairo University and PhD from Emory University. I work in Computational Pathology.
Twitter: mtageld
Location: Chicago, IL, USA
Blog: www.mamgad.com
Mohamed Amgad Tageldin's Projects
Software and documentation from the active learning project on interactive classification.
A Python implementation of global optimization with gaussian processes.
BMI 500: Introduction to Biomedical Informatics
This is the framework with 17 existing crowdsourced truth inference algorithms.
FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.
The official deployment of the Digital Slide Archive and HistomicsTK.
Extending Ripley’s K-Function to Quantify Aggregation in 2-D Grayscale Images
Mask RCNN in TensorFlow
Fully Connected DenseNet for Image Segmentation (https://arxiv.org/pdf/1611.09326v1.pdf)
Tools for interaction with various healthcare databases, including CDC, chicagohealthatlas etc
a HDF5-based python pickle replacement
Histomics analyses so far
HistoQC is an open-source quality control tool for digital pathology slides
Cluster images based on image content using a pre-trained deep neural network and hierarchical clustering
shell scripts to install different version of OpenCV in different distributions of Linux
This code is for an introduction to Git at Emory University.
Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
Codes for our paper "Revisiting Multiple Instance Neural Networks".
Multiple-Instance Support Vector Machines
Implementation of the NCA in python with scipy and numpy
Deep Learning libraries tested on images and time series
Build and run Docker containers leveraging NVIDIA GPUs
Python wrapper to Philipp Krähenbühl's dense (fully connected) CRFs with gaussian edge potentials.
PyTorch tutorial for PyData Berlin
Tools for tissue image stain normalization and augmentation in Python 3. [python, scientific]
VIP cheatsheets for Stanford's CS 230 Deep Learning
Ten simple rules for better figures
Computation using data flow graphs for scalable machine learning