ML-based medical imaging using Azure

Overview

Disclaimer
This code is provided for research and development use only. This code is not intended for use in clinical decision-making or for any other clinical use and the performance of the code for clinical use has not been established.

Medical Imaging with Azure Machine Learning Demos

Welcome to our medical imaging demo repository! The content includes several Python notebooks that cover medical imaging use cases based on classification, object detection and instance segmentation.

All use cases are based on publicly available datasets like brain RMI scans, cell micrographs, chest x-ray images and more. Since we cannot distribute the data directly, we refer to publicly available download locations.

The purpose of our notebooks is to demonstrate how Azure Machine Learning can be used to support medical imaging and other use cases in areas like data and model management, deployment, experiment tracking and explainability. Furthermore, we cover various data science approaches ranging from manual model development with PyTorch to automated machine learning for images. Another focus is to provide MLOPS based examples for automating the machine learning lifecycle for medical use cases including retraining when new data becomes available.

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.

Owner
Microsoft Azure
APIs, SDKs and open source projects from Microsoft Azure
Microsoft Azure
Leveraging Social Influence based on Users Activity Centers for Point-of-Interest Recommendation

SUCP Leveraging Social Influence based on Users Activity Centers for Point-of-Interest Recommendation () Direct Friends (i.e., users who follow each o

Kosar 8 Nov 26, 2022
Complex Answer Generation For Conversational Search Systems.

Complex Answer Generation For Conversational Search Systems. Code for Does Structure Matter? Leveraging Data-to-Text Generation for Answering Complex

Hanane Djeddal 0 Dec 06, 2021
Differentiable Abundance Matching With Python

shamnet Differentiable Stellar Population Synthesis Installation You can install shamnet with pip. Installation dependencies are numpy, jax, corrfunc,

5 Dec 17, 2021
This implements one of result networks from Large-scale evolution of image classifiers

Exotic structured image classifier This implements one of result networks from Large-scale evolution of image classifiers by Esteban Real, et. al. Req

54 Nov 25, 2022
R-package accompanying the paper "Dynamic Factor Model for Functional Time Series: Identification, Estimation, and Prediction"

dffm The goal of dffm is to provide functionality to apply the methods developed in the paper “Dynamic Factor Model for Functional Time Series: Identi

Sven Otto 3 Dec 09, 2022
MoCoPnet - Deformable 3D Convolution for Video Super-Resolution

MoCoPnet: Exploring Local Motion and Contrast Priors for Infrared Small Target Super-Resolution Pytorch implementation of local motion and contrast pr

Xinyi Ying 28 Dec 15, 2022
Use AI to generate a optimized stock portfolio

Use AI, Modern Portfolio Theory, and Monte Carlo simulation's to generate a optimized stock portfolio that minimizes risk while maximizing returns. Ho

Greg James 30 Dec 22, 2022
An all-in-one application to visualize multiple different local path planning algorithms

Table of Contents Table of Contents Local Planner Visualization Project (LPVP) Features Installation/Usage Local Planners Probabilistic Roadmap (PRM)

Abdur Javaid 47 Dec 30, 2022
Automatic caption evaluation metric based on typicality analysis.

SeMantic and linguistic UndeRstanding Fusion (SMURF) Automatic caption evaluation metric described in the paper "SMURF: SeMantic and linguistic UndeRs

Joshua Feinglass 6 Jan 09, 2022
Recommendation algorithms for large graphs

Fast recommendation algorithms for large graphs based on link analysis. License: Apache Software License Author: Emmanouil (Manios) Krasanakis Depende

Multimedia Knowledge and Social Analytics Lab 27 Jan 07, 2023
(JMLR'19) A Python Toolbox for Scalable Outlier Detection (Anomaly Detection)

Python Outlier Detection (PyOD) Deployment & Documentation & Stats Build Status & Coverage & Maintainability & License PyOD is a comprehensive and sca

Yue Zhao 6.6k Jan 03, 2023
UnsupervisedR&R: Unsupervised Pointcloud Registration via Differentiable Rendering

UnsupervisedR&R: Unsupervised Pointcloud Registration via Differentiable Rendering This repository holds all the code and data for our recent work on

Mohamed El Banani 118 Dec 06, 2022
SMCA replication There are no extra compiled components in SMCA DETR and package dependencies are minimal

Usage There are no extra compiled components in SMCA DETR and package dependencies are minimal, so the code is very simple to use. We provide instruct

22 May 06, 2022
PyTorch implementation of the Transformer in Post-LN (Post-LayerNorm) and Pre-LN (Pre-LayerNorm).

Transformer-PyTorch A PyTorch implementation of the Transformer from the paper Attention is All You Need in both Post-LN (Post-LayerNorm) and Pre-LN (

Jared Wang 22 Feb 27, 2022
Graph Convolutional Networks in PyTorch

Graph Convolutional Networks in PyTorch PyTorch implementation of Graph Convolutional Networks (GCNs) for semi-supervised classification [1]. For a hi

Thomas Kipf 4.5k Dec 31, 2022
Official PyTorch implementation of the paper "TEMOS: Generating diverse human motions from textual descriptions"

TEMOS: TExt to MOtionS Generating diverse human motions from textual descriptions Description Official PyTorch implementation of the paper "TEMOS: Gen

Mathis Petrovich 187 Dec 27, 2022
Segcache: a memory-efficient and scalable in-memory key-value cache for small objects

Segcache: a memory-efficient and scalable in-memory key-value cache for small objects This repo contains the code of Segcache described in the followi

TheSys Group @ CMU CS 78 Jan 07, 2023
Official PyTorch code of DeepPanoContext: Panoramic 3D Scene Understanding with Holistic Scene Context Graph and Relation-based Optimization (ICCV 2021 Oral).

DeepPanoContext (DPC) [Project Page (with interactive results)][Paper] DeepPanoContext: Panoramic 3D Scene Understanding with Holistic Scene Context G

Cheng Zhang 66 Nov 16, 2022
A GridMixup augmentation, inspired by GridMask and CutMix

GridMixup A GridMixup augmentation, inspired by GridMask and CutMix Easy install pip install git+https://github.com/IlyaDobrynin/GridMixup.git Overvie

IlyaDo 42 Dec 28, 2022
Personal thermal comfort models using digital twins: Preference prediction with BIM-extracted spatial-temporal proximity data from Build2Vec

Personal thermal comfort models using digital twins: Preference prediction with BIM-extracted spatial-temporal proximity data from Build2Vec This repo

Building and Urban Data Science (BUDS) Group 5 Dec 02, 2022