8
Cooperating Clinics
247
Incoming Datasets
245
Number of Pre-Registrations
Medical imaging is potentially well suited for Covid-19 diagnosis. This challenge is about connecting the best brains to support doctors with artificial intelligence systems.
Our Motivation
Medical Imaging
Medical imaging, in particular CT, is potentially well suited for Covid-19 detection and diagnosis. It could be faster than “traditional” testing.
Deep Learning
The first applications of deep learning on CT data show a promising result. Studies, however, published no benchmarks and only a few models.
Data Labeling
High quality labeled multicenter training data is not accessible to developers. Regulations and computing resources are a challenge!
Artifical Intelligence
AI on images is useful beyond diagnosis. Prediction of patient permanence in the ICU, disease progression and survival analysis are key to save lives.
Our Plan
Build a dataset
Multi-center imaging dataset obtained from various centers globally
Allow access
Full access for data contributors (hospitals) early on. Supervised access for third parties.
Provide compute
People submit their model on shared computational resources provided by us.
Initiators and Supporters
The Challenge
The challenge aims to aid radiologists and physicians through objective and quantitative computational assessment of chest imaging in the context of COVID-19. We provide access to a large dataset of 3D chest CT imaging of the lung, collected from several European and international radiological centers. We call the international research community to develop and test artificial intelligence algorithms on this dataset.
The Challenge Comes In Two Stages
Phase 1 Dataset Aggregation
Dataset aggregation from various global clinical partners. Our contributing partners (academic institutions providing patient data) have the right to access the entire database and can begin their research. Clinicians and scientists will develop goals and hypotheses that will define the challenges.
Phase 2 Coding
Third parties can contribute and run their code on the datasets without downloading them. Thus, patient rights are protected while maintaining maximum data access.
Timeline 2020
March 30
Data collection starts: Data collection in biobank. Data curation and standardization.
April 6
Stage 1: First batch of data has been provided. Collaborative definition of the challenges. Data labeling.
August 3
Stage 2: Challenge announced. Challenge registration open.
August 17
Official start of the challenge. Kick-off event live on YouTube (at 16:00 CEST). Opening job submission platform.
November 30
Deadline for the submission of data for the challenge.
Join the fight against Covid-19
Who Can Participate?
Initially, access will be reserved to research groups that are able to contribute data (at least a total of 200 cases split into COVID19 and control cases → see data). Later, access will be extended. This will allow ramping up infrastructure.
Academic Radiology Departments
Contribute data + define challenges, full access
Radiology departments and private practices
Donate datasets of your cases to help!
Deep learning experts from any background
Use your skills on the entire dataset!
Join the fight against Covid-19
We Are Looking For High Quality Data
Collection and Curation
We accept anonymized DICOM datasets stripped of any patient information + clinical metadata (we provide a form) containing the parameters on the right:
One dataset can contain one or several chest CT scans of the same patient (indicate on what day since hospital admission it was taken).
Clinical metadata (example)
- sex (m/f/d)
- age (in years at the time of admission)
- PCR confirmed positive / negative / not available
- clinical condition (outpatient, inpatient, intermediate care, intensive care)
- days since admission to hospital
- (pre-existing relevant health conditions)
Curation
- Standardize data format
- Quality control
- Standardize metadata
Data levels
Increasing capabilities
With the progress of the project, we aim to instruct and help our clinical partners to extend their datasets by additional clinical variables and metrics acquired throughout the treatment process.
Level I (baseline parameters):
- PCR CoViD-19 test positivity (yes/no/not available) – date relative to imaging
- Hospitalization status at time of imaging (ER/inpatient/IMC/ICU)
- Day of imaging after admission to hospital
Level II (Progression of disease):
- Follow-up imaging (CT and X-Rays)
- Including+ Level I data
Level III (outcome and course of treatment):
- Outcome
- Length of hospital stay (days)
- Discharge condition (recovered, fatal, transfer to other clinic)
- Ventilation period (day start to day stop) (if applicable)
- Monitoring:
- Initial pO2 if available
- Initial CRP and blood count (or full labs) if available
- Vit. D level if available
- Comorbidities
- Other Pneumonia (no/previous/currently active)
- Cause (bacterial/viral/fungal)
- Chronic lung disease (yes/no)
- Heart conditions (none/mild/moderate/severe)
- Immunocompromised (yes/no)
- Diabetes (yes/no)
- Other Pneumonia (no/previous/currently active)
Join the fight against Covid-19
Patient Data Protection and Ethics
Data Collection Process
Dissemination
Reproducible science
It is important that the challenge creates value for patients and caregivers. And provides opportunity for future research. That’s why the winning algorithms will be made open-source and available for public research use.
Publication
- We encourage and support the teams to publish their results
Models/Networks
- The resulting models will be made open-source and publicly available on GitHub
Benchmark datasets
- Science never stops. Our tested COVID-19 benchmark will be publicly available after challenge conclusion. Thus, we create and maintain a gold-standard performance test for future research.
Join the fight against Covid-19
Team
We are a global, interdisciplinary team driven by one goal
My name is Linda bij de Leij and I work at the University Medical Center Groningen (UMCG), in the Netherlands. There, I'm a communication officer for the Data Science Center in Health (DASH) and the Research IT-programme. For the Covid19Challenge I will, among other things, keep you updated on the progress of the challenge through the website, social media and email updates. I am happy to join the team!
Fausto Milletarì is the Applied AI Lead at Verb Surgical Inc. For the Covid19Challenge, he is the mastermind behind the whole Amazon Web Services (AWS) and Eisen infrastructure that is making this challenge possible.
Thomas Heiliger works at Brainlab for several years and has deep inside in digital surgery and medicine from an industry point of view.
In addition, he is working at the Ludwig-Maximilians Universität München on his doctoral thesis on a scientific project related to research in the field of augmented reality in minimally invasive abdominal surgery. The preparation and autonomous enrichment of medical image data with 3D objects are one of his fields of research.
Regarding the challenge, he will be taking part in the labeling process of the data and push collaboration with external industry partners.
In addition, he is working at the Ludwig-Maximilians Universität München on his doctoral thesis on a scientific project related to research in the field of augmented reality in minimally invasive abdominal surgery. The preparation and autonomous enrichment of medical image data with 3D objects are one of his fields of research.
Regarding the challenge, he will be taking part in the labeling process of the data and push collaboration with external industry partners.
Szilard Szabo is a doctor of medical informatics and a physician in training at Ludwig-Maximilians Universität München, as well as team leader digital biobank at M3i Industry-in-Clinic Platform. For the Covid19Challenge, he is responsible for the data teams and helping the clinics to transfer their data.
Annika Reinke is a PhD student at the DKFZ German Cancer Research Center under the supervision of Prof. Lena Maier-Hein, one of the scientific advisors in our challenge. She is specializing in formal guidelines - the do's and don'ts - of public challenge design, which is also how she will contribute to the Covid19Challenge.
Leyla Ghaffari has several years of experience in nonprofit and volunteer management. Her professional career has focused on infectious disease prevention and issues related to homelessness. She is currently studying at San Francisco State University and plans to start medical school in the near future. For the Covid-19 Challenge, she is assisting with various tasks including organizational and logistical support.
Seyed-Ahmad Ahmadi is an AI scientist from Ludwig-Maximilians Universität München. Within the Covid19Challenge, he advised on structuring the process and he is, together with the Technical University of Munich, in charge of building the reference model.
Stefan Taing is a partner at Munich Innovation Group GmbH and CEO & co-founder of M3i Industry-in-Clinic Platform. For the Covid19Challenge, he takes care of many administrative tasks and provides some of his employees to work for the challenge.
Felix Swamy v. Zastrow is a fifth-year medical student at Ludwig-Maximilians Universität München and joined the M3i Industry-in-Clinic Platform a year ago as part of the segmentation team. He takes part in the specification of data and is responsible for the training of labeling specialists and quality control. For the Covid19Challenge, he is involved in the segmentation and labeling of the data as well.
Gergely Dietz is a Medical Doctor and CEO at Neumann Medical, which we proudly present as one of our new partners. Neumann Medical is a health IT company, developing a disruptive new method for the generation and collection of medical data. By using structured reporting and standardized image annotation, the company supports the Covid19Challenge by collecting and digitizing Covid-data from medical institutes in the CEE region.
Andreas Liebl is the managing director at UnternehmerTUM and head of the appliedAI Initiative - UnternehmerTUM. For the Covid19Challenge, he and the appliedAI team can provide support on the data collection and marketing of the challenge. We are happy to have them on board!
Anees Kazi is a PhD @ Computer Aided Medical Procedures & Augmented Reality, Technical University of Munich working on Geometric Deep Learning for Healthcare. She is one of the student co-ordinator in our Covid19Challenge mainly from the technical and community building side.
Simon Weidert works at the University Clinic of the Ludwig-Maximilians Universität München as an orthopedic trauma and spine specialist and he is also founder and co-CEO of the M3i Industry-in-Clinic Platform. He is part of the core team that had the idea to use an existing digital biobank solution in a way that creates a challenge that allows teams from all over the world to participate and crowd-source solutions for clinical problems. Simon firmly believes that using artificial intelligence can contribute to solving specific problems that arise in the current pandemic, which is his main motivation for the COVID-19 Challenge The method used in this challenge, could be applied to other future problems as well, because a crowd will always be smarter than an individual.
Jens Elsner is a partner engineering services and industry expert telecommunications at Munich Innovation Group GmbH and CEO of Munich Innovation Labs. For the Covid19Challenge, he does the project management and operations of the challenges. He leads his own AI team at Munich Innovation Labs, which helps us with IT tasks.
Wolfgang Männel is the managing director of Tathros Innovation and Tathros Digital, as well as senior partner at Munich Innovation Group GmbH. For the Covid19Challenge, he is an advisor and he helped to create and set up the website for the challenge.
Pál Maurovich Horvat is the director of the Medical Imaging Centre and chairman of Radiology at the Semmelweis University, Budapest, Hungary. Dr. Maurovich Horvat is the elected vice president for nuclear cardiology & cardiac CT at the European Association of Cardiovascular Imaging (EACVI). He has graduated from Semmelweis University and from Harvard University. He is the author of more than 140 papers with over 5000 citations. Dr. Maurovich Horvat helps to coordinate the Covid19Challenge network in Central and Eastern Europe.
The Medical Imaging Centre of the Semmelweis University is a high volume institution with three departments: Radiology, Nuclear Medicine and Neuroradiology. The Medical Imaging Centre is the diagnostic imaging centre for COVID-19 patients at the Semmelweis University.
Istvan Köveshazi is a fifth-year medical student and doctoral candidate at the Ludwig-Maximilians Universität München. His doctoral thesis is about pedicle screw planning in the spine surgery using artificial intelligence. He also works on several AI projects at M3i Industry-in-Clinic Platform which require the implementation of medically correct segmentation. For the Covid19Challenge, he is responsible for data sorting, anonymization and categorization, and he is the contact person for questions regarding our anonymization tool. He also played a part in defining the segmentation procedure, for example the selection of the most suitable medical imaging software for our project.
Scientific Advisory Committee
Our team is advised by
Dr méd. Amine Korchi is an entrepreneurial-minded medical doctor based in Switzerland and specialised in Neuroradiology & Musculoskeletal imaging and interventions. He has a passion for innovation and has developed over time an expertise in the application of cutting-edge technologies in healthcare and life sciences. He had multiple non-clinical professional experiences during his career, from scientist and principal investigator to strategy consulting and venture capital. He is currently Venture Partner at Fusion Partners and Managing Partner at Singularity Consulting in Switzerland, where he advises established corporates, selected startups and investment funds about technology innovation and ventures. Dr méd. Amine Korchi still enjoys and practices Radiology part-time.
Debdoot Sheet is an assistant professor at the Indian Institute of Technology in Kharagpur. He is an expert on deep learning and medical imaging and for the Covid19Challenge he is part of the scientific advisory committee.
Prof. Michael Friebe is holding a chair at the University of Magdeburg, is an entrepreneur, inventor and investor. We love his yearly, famous Radiological Society of North America (RSNA) reports. Now he will contribute his knowledge in AI and the radiologic imaging field in order to define relevant and achievable goals for our worldwide challenge!
Franz Pfister is a co-founder and CEO of deepc, a health AI startup company based in Munich. He is an entrepreneur, medical doctor and data scientist experienced in AI and for the Covid19Challenge he is part of the scientific advisory committee.
Daniel received his habilitation at Heidelberg University in the field of machine learning and data science. His startup Pallas Ludens enabled automotive and medical imaging companies to collect large machine training datasets.
After about three years, in 2016, he and his team joined Apple, where he worked for three years on dataset design, annotation and data quality for computer vision tasks.
Today, Daniel works as Business Angel with a number of investments in games and machine learning. He also founded two new companies he is leading as managing director: one is about dataset quality assurance (Quality Match GmbH) and one is an app named Flax which facilitates friend meetup organization.
After about three years, in 2016, he and his team joined Apple, where he worked for three years on dataset design, annotation and data quality for computer vision tasks.
Today, Daniel works as Business Angel with a number of investments in games and machine learning. He also founded two new companies he is leading as managing director: one is about dataset quality assurance (Quality Match GmbH) and one is an app named Flax which facilitates friend meetup organization.
Antony Hodgson is a professor of Mechanical/Biomedical Engineering and Associate Director at the Institute for Computing, Information and Cognitive Systems at The University of British Columbia. For the Covid19Challenge, he is part of the Scientific Advisory Commission.
Prof. Lena Maier-Hein is from the DKFZ German Cancer Research Center. She has a focus on data science in computer assisted interventions and will advise us in particular on the challenge design.
Nassir Navab is chairholder, computer aided medical procedure & augmented reality at Technical University of Munich. For the Covid19Challenge, he contributes to building the reference model in cooperation with Ludwig-Maximilians Universität München.