Vignesh Sivan's repositories
NeurReg_Pytorch
An unofficial implementation of NeurReg: Neural Registration and Its Application to Image Segmentation by Zhu et al.
ANTs
Advanced Normalization Tools (ANTs)
CLAM
Data-efficient and weakly supervised computational pathology on whole slide images - Nature Biomedical Engineering
convnets_c
A C Tensor Library For building ConvNets
copilot.vim
Neovim plugin for GitHub Copilot
crafting-interpreters
Learning how to craft interpreters
dipy
DIPY is the paragon 3D/4D+ imaging library in Python. Contains generic methods for spatial normalization, signal processing, machine learning, statistical analysis and visualization of medical images. Additionally, it contains specialized methods for computational anatomy including diffusion, perfusion and structural imaging.
direct
Deep learning framework for MRI reconstruction
fastMRI
A large-scale dataset of both raw MRI measurements and clinical MRI images.
fleet-theme-nvim
A port of Fleet's default theme for Neovim
img-comparison-slider
Image comparison slider. Compare images before and after. Supports React, Vue, Angular.
inpainting_gmcnn
Image Inpainting via Generative Multi-column Convolutional Neural Networks, NeurIPS2018
LapIRN
Large Deformation Diffeomorphic Image Registration with Laplacian Pyramid Networks
MIDLLatexTemplate
Latex template for the MIDL Conference
MONAI
AI Toolkit for Healthcare Imaging
p_tqdm
Parallel processing with progress bars
pdd_net
Probabilistic Dense Displacement Network (3D discrete deep learning registration)
RN
Region Normalization for Image Inpainting, accepted by AAAI-2020
SimpleITK
SimpleITK: a layer built on top of the Insight Toolkit (ITK), intended to simplify and facilitate ITK's use in rapid prototyping, education and interpreted languages.
spinalcordtoolbox
Comprehensive and open-source library of analysis tools for MRI of the spinal cord.
tutorials
MONAI Tutorials
voxelmorph
Implements semi-supervised training scripts in Torch
WSITools
Tools for whole slide image (WSI) processing. Especially for (pairwise) patch extraction, annotation parsing and data preparation for deep learning purposes.