Juan Pablo de la Cruz Gutiérrez's Projects
This is the official pytorch implementation for the paper: Instance Similarity Learning for Unsupervised Feature Representation.
Fork of https://github.com/d3x0r/MarchingTetrahedra - adds texturing test
A fully invertible U-Net for memory efficiency in Pytorch.
An easy-to-read, quick reference for JS best practices, accepted coding standards, and links around the Web
Config files for my GitHub profile.
Cloud Native Presentation Slides with Jupyter Notebook + Reveal.js
Demonstration of parametric bootstrap to find k for kmeans
Website for KDD 2020 Tutorial on User-centric xai for healthcare
Some fun things you can do with kernel modules (all "bad ideas")
Skeletonize densely labeled 3D image segmentations.
Toolchain for converting LaTeX Book documents to ebook formats
Source code for "PointTriNet: Learned Triangulation of 3D Point Sets", by Nicholas Sharp and Maks Ovsjanikov at ECCV 2020
C++ header-only library with methods to efficiently encode/decode Morton codes in/from 2D/3D coordinates
Fork of test page of ... (update later)
Site web of the Mathematical Tours
Implementation of Dual Marching Cubes with automatic lookup table generation
The Medical Detection Toolkit contains 2D + 3D implementations of prevalent object detectors such as Mask R-CNN, Retina Net, Retina U-Net, as well as a training and inference framework focused on dealing with medical images.
Uncertainty in Medical Image Analysis
Library to load a DLL from memory.
Code for "MeshSDF: Differentiable Iso-Surface Extraction", NeurIPS2020, SpotLight
MICCAI 2019 Open Source Papers
Code for tutorial at MICCAI 2019
A fast, extensible and spec-compliant Markdown parser in pure Python.
The fastest markdown parser in pure Python with renderer feature.
:book: Notes and summaries of some Machine Learning / Computer Vision / NLP papers.
This repository contains all the material for the MLTrain NIPS workshop
Demonstration for Mobius transformations on images
AI Toolkit for Healthcare Imaging
MONAI Tutorials
Optimization learning for image registration.