- Baltimore, MD
Highlights
- Pro
Stars
PyTorch Implementation of "Resource Efficient 3D Convolutional Neural Networks", codes and pretrained models.
Scikit-learn compatible decision trees beyond those offered in scikit-learn
ilastik-shell, applets, and workflows to string them together.
Curve Matching using the Square Root Velocity Functions
WebGL-based viewer for volumetric data
Method container for computational neuroscience on brains.
Template for an academic poster using Marp
A paper investigating bilateral symmetry of connectome networks.
Skeletonize densely labeled 3D image segmentations with TEASAR. (Medial Axis Transform)
Read and write Neuroglancer datasets programmatically.
napari: a fast, interactive, multi-dimensional image viewer for python
Collection of educational resources. Primarily focused on skills that are helpful in the NeuroData lab or the Neuro Data Design course at Johns Hopkins University.
Python package for multivariate hypothesis testing
Download files from Google Drive using Python 2 or Python 3
Python package for graph statistics