The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

an image registration/augmentation/segmentation package

This repository includes the official project of TransUNet, presented in our paper: TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.

Projects and exercises for the latest Deep Learning ND program https://www.udacity.com/course/deep-learning-nanodegree--nd101

A pytorch-based deep learning framework for multi-modal 2D/3D medical image segmentation

An image registration method using convolutional neural network features.

Medical Imaging Deep Learning library to train and deploy 3D segmentation models on Azure Machine Learning

PyTorch implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ....

image registration related books, papers, videos, and toolboxes

[ICCV 2019] Recursive Cascaded Networks for Unsupervised Medical Image Registration

PyTorch3D is FAIR's library of reusable components for deep learning with 3D data

Contains high quality implementations of Deep Reinforcement Learning algorithms written in PyTorch

AI Toolkit for Healthcare Imaging

Exercise notebooks for CVND.

An open-source Python framework for hybrid quantum-classical machine learning.

a project for developing registration tools with convolutional neural networks

A PyTorch-based library for working with 3D and 2D convolutional neural networks, with focus on semantic segmentation of volumetric biomedical image data

PyTorch implementations of Generative Adversarial Networks.

Code for replication of the paper "The relativistic discriminator: a key element missing from standard GAN"

A Deep Learning method to segment punctate white matter lesions (PWMLs); Brain tumor segmentation;

U-Net Biomedical Image Segmentation

[TMI'20] Unpaired Multi-modal Segmentation via Knowledge Distillation

Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams

A collection of extensions and data-loaders for few-shot learning & meta-learning in PyTorch

Systems and Networking related Video research published in major venues of Computer Science.

A guideline for building practical production-level deep learning systems to be deployed in real world applications.

An example cross-platform CMake-based project using SDL2 and OpenGL