Baseline inference Algorithm for the STOIC2021 challenge.

Overview

STOIC2021 Baseline Algorithm

This codebase contains an example submission for the STOIC2021 COVID-19 AI Challenge. As a baseline algorithm, it implements a simple evaluation pipeline for an I3D model that was trained on the STOIC2021 training data. You can use this repo as a template for your submission to the Qualification phase of the STOIC2021 challenge.

If something does not work for you, please do not hesitate to contact us or add a post in the forum. If the problem is related to the code of this repository, please create a new issue on GitHub.

Table of Contents

Before implementing your own algorithm with this template, we recommend to first upload a grand-challenge.org Algorithm based on the unaltered template by following these steps:

Afterwards, you can easily implement your own algorithm, by altering this template and updating the Algorithm you created on grand-challenge.org.

Prerequisites

We recommend using this repository on Linux. If you are using Windows, we recommend installing Windows Subsystem for Linux (WSL). Please watch the official tutorial by Microsoft for installing WSL 2 with GPU support.

  • Have Docker installed.
  • Have an account on grand-challenge.org and make sure that you are a verified user there.

Building, testing, and exporting your container

Building

To test if your system is set up correctly, you can run ./build.sh (Linux) or ./build.bat (Windows), that simply implement this command:

docker build -t stoicalgorithm .

Please note that the next step (testing the container) also runs a build, so this step is not necessary if you are certain that everything is set up correctly.

Testing

To test if the docker container works as expected, test.sh/test.bat will build the container and run it on images provided in the ./test/ folder. It will then check the results (.json files produced by your algorithm) against the .json files in ./test/.

If the tests run successfully, you will see Tests successfully passed....

Note: If you do not have a GPU available on your system, remove the --gpus all flag in test.sh/test.bat to run the test. Note: When you implemented your own algorithm using this template, please update the the .json files in ./test/ according to the output of your algorithm before running test.sh/test.bat.

Exporting

Run export.sh/export.bat to save the docker image to ./STOICAlgorithm.tar.gz. This script runs build.sh/build.bat as well as the following command: docker save stoicalgorithm | gzip -c > STOICAlgorithm.tar.gz

Creating an Algorithm on grand-challenge.org

After building, testing, and exporting your container, you are ready to create an Algorithm on grand-challenge.org. Note that there is no need to alter the algorithm implemented in this baseline repository to start this step. Once you have created an Algorithm on grand-challenge.org, you can later upload new docker containers to that same Algorithm as many times as you wish.

You can create an Algorithm by following this link. Some important fields are:

  • Please choose a Title and Description for your algorithm;
  • Enter CT at Modalities and Lung (Thorax) at Structures;
  • Select a logo to represent your algorithm (preferably square image);
  • For the interfaces of the algorithm, please select CT Image as Inputs, and as Outputs select both Probability COVID-19 and Probability Severe COVID-19;
  • Choose Viewer CIRRUS Core (Public) as a Workstation;
  • At the bottom of the page, indicate that you would like your Docker image to use GPU and how much memory it needs. After filling in the form, click the "Save" button at the bottom of the page to create your Algorithm.

Uploading your container to your Algorithm

Uploading manually

You have now built, tested, and exported your container and created an Algorithm on grand-challenge.org. To upload your container to your Algorithm, go to "Containers" on the page for your Algorithm on grand-challenge.org. Click on "upload a Container" button, and upload your .tar.gz file. You can later update your container by uploading a new .tar.gz file.

Linking a GitHub repo

Instead of uploading the .tar.gz file directly, you can also link your GitHub repo. Once your repo is linked, grand-challenge.org will automatically build the docker image for you, and add the updated container to your Algorithm.

  • First, click "Link Github Repo". You will then see a dropdown box, where your Github repo is listed only if it has the Grand-Challenge app already installed. Usually this is not the case to begin with, so you should click on "link a new Github Repo". This will guide you through the installation of the Grand-challenge app in your repository.
  • After the installation of the app in your repository is complete you should be automatically returned to the Grand Challenge page, where you will find your repository now in the dropdown list (In the case you are not automatically returned to the same page you can find your algorithm and click "Link Github Repo" again). Select your repository from the dropdown list and click "Save".
  • Finally, you need to tag your repository, this will trigger Grand-Challenge to start building the docker container.

Make sure your container is Active

Please note that it can take a while until the container becomes active (The status will change from "Ready: False" to "Active") after uploading it, or after linking your Github repo. Check back later or refresh the URL after some time.

Submitting to the STOIC2021 Qualification phase

With your Algorithm online, you are ready to submit to the STOIC2021 Qualification Leaderboard. On https://stoic2021.grand-challenge.org/, navigate to the "Submit" tab. Navigate to the "Qualification" tab, and select your Algorithm from the drop down list. You can optionally leave a comment with your submission.

Note that, depending on the availability of compute nodes on grand-challenge.org, it may take some time before the evaluation of your Algorithm finishes and its results can be found on the Leaderboard.

Implementing your own algorithm

You can implement your own solution by editing the predict function in ./process.py. Any additional imported packages should be added to ./requirements.txt, and any additional files and folders you add should be explicitly copied in the ./Dockerfile. See ./requirements.txt and ./Dockerfile for examples. To update your algorithm, you can simply test and export your new Docker container, after which you can upload it to your Algorithm. Once your new container is Active, you can resubmit your Algorithm.

Please note that your container will not have access to the internet when executing on grand-challenge.org, so all model weights must be present in your container image. You can test this locally using the --network=none option of docker run.

Good luck with the STOIC2021 COVID-19 AI Challenge!

Tip: Running your algorithm on a test folder:

Once you validated that the algorithm works as expected in the Testing step, you might want to simply run the algorithm on the test folder and check the output .json files for yourself. If you are on a native Linux system you will need to create a results folder that the docker container can write to as follows (WSL users can skip this step).

mkdir ./results
chmod 777 ./results

To write the output of the algorithm to the results folder use the following command:

docker run --rm --memory=11g -v ./test:/input/ -v ./results:/output/ STOICAlgorithm
Owner
Luuk Boulogne
Luuk Boulogne
A Large Scale Benchmark for Individual Treatment Effect Prediction and Uplift Modeling

large-scale-ITE-UM-benchmark This repository contains code and data to reproduce the results of the paper "A Large Scale Benchmark for Individual Trea

10 Nov 19, 2022
PIKA: a lightweight speech processing toolkit based on Pytorch and (Py)Kaldi

PIKA: a lightweight speech processing toolkit based on Pytorch and (Py)Kaldi PIKA is a lightweight speech processing toolkit based on Pytorch and (Py)

336 Nov 25, 2022
Code for CPM-2 Pre-Train

CPM-2 Pre-Train Pre-train CPM-2 此分支为110亿非 MoE 模型的预训练代码,MoE 模型的预训练代码请切换到 moe 分支 CPM-2技术报告请参考link。 0 模型下载 请在智源资源下载页面进行申请,文件介绍如下: 文件名 描述 参数大小 100000.tar

Tsinghua AI 136 Dec 28, 2022
OBG-FCN - implementation of 'Object Boundary Guided Semantic Segmentation'

OBG-FCN This repository is to reproduce the implementation of 'Object Boundary Guided Semantic Segmentation' in http://arxiv.org/abs/1603.09742 Object

Jiu XU 3 Mar 11, 2019
Learning an Adaptive Meta Model-Generator for Incrementally Updating Recommender Systems

Learning an Adaptive Meta Model-Generator for Incrementally Updating Recommender Systems This is our experimental code for RecSys 2021 paper "Learning

11 Jul 28, 2022
Implementation of H-Transformer-1D, Hierarchical Attention for Sequence Learning using 🤗 transformers

hierarchical-transformer-1d Implementation of H-Transformer-1D, Hierarchical Attention for Sequence Learning using 🤗 transformers In Progress!! 2021.

MyungHoon Jin 7 Nov 06, 2022
Goal of the project : Detecting Temporal Boundaries in Sign Language videos

MVA RecVis course final project : Goal of the project : Detecting Temporal Boundaries in Sign Language videos. Sign language automatic indexing is an

Loubna Ben Allal 6 Dec 21, 2022
Code accompanying "Evolving spiking neuron cellular automata and networks to emulate in vitro neuronal activity," accepted to IEEE SSCI ICES 2021

Evolving-spiking-neuron-cellular-automata-and-networks-to-emulate-in-vitro-neuronal-activity Code accompanying "Evolving spiking neuron cellular autom

SOCRATES: Self-Organizing Computational substRATES 2 Dec 02, 2022
Pocsploit is a lightweight, flexible and novel open source poc verification framework

Pocsploit is a lightweight, flexible and novel open source poc verification framework

cckuailong 208 Dec 24, 2022
Transformer based SAR image despeckling

Transformer based SAR image despeckling Using the code: The code is stable while using Python 3.6.13, CUDA =10.1 Clone this repository: git clone htt

27 Nov 13, 2022
Implementation of TimeSformer, a pure attention-based solution for video classification

TimeSformer - Pytorch Implementation of TimeSformer, a pure and simple attention-based solution for reaching SOTA on video classification.

Phil Wang 602 Jan 03, 2023
A PyTorch Toolbox for Face Recognition

FaceX-Zoo FaceX-Zoo is a PyTorch toolbox for face recognition. It provides a training module with various supervisory heads and backbones towards stat

JDAI-CV 1.6k Jan 06, 2023
Decision Transformer: A brand new Offline RL Pattern

DecisionTransformer_StepbyStep Intro Decision Transformer: A brand new Offline RL Pattern. 这是关于NeurIPS 2021 热门论文Decision Transformer的复现。 👍 原文地址: Deci

Irving 14 Nov 22, 2022
Official Pytorch Implementation of Adversarial Instance Augmentation for Building Change Detection in Remote Sensing Images.

IAug_CDNet Official Implementation of Adversarial Instance Augmentation for Building Change Detection in Remote Sensing Images. Overview We propose a

53 Dec 02, 2022
PyTorch reimplementation of minimal-hand (CVPR2020)

Minimal Hand Pytorch Unofficial PyTorch reimplementation of minimal-hand (CVPR2020). you can also find in youtube or bilibili bare hand youtube or bil

Hao Meng 228 Dec 29, 2022
Compartmental epidemic model to assess undocumented infections: applications to SARS-CoV-2 epidemics in Brazil - Datasets and Codes

Compartmental epidemic model to assess undocumented infections: applications to SARS-CoV-2 epidemics in Brazil - Datasets and Codes The codes for simu

1 Jan 12, 2022
Official Pytorch implementation of RePOSE (ICCV2021)

RePOSE: Iterative Rendering and Refinement for 6D Object Detection (ICCV2021) [Link] Abstract We present RePOSE, a fast iterative refinement method fo

Shun Iwase 68 Nov 15, 2022
Cookiecutter PyTorch Lightning

Cookiecutter PyTorch Lightning Instructions # install cookiecutter pip install cookiecutter

Mazen 8 Nov 06, 2022
Inteligência artificial criada para realizar interação social com idosos.

IA SONIA 4.0 A SONIA foi inspirada no assistente mais famoso do mundo e muito bem conhecido JARVIS. Todo mundo algum dia ja sonhou em ter o seu própri

Vinícius Azevedo 2 Oct 21, 2021