Runmila AI Institute & minoHealth AI Labs Tuberculosis Classification via X-Rays Challenge
Build an AI system that can classify Tuberculosis and Normal X-Ray images
Prize
2000 Zindi Points
Time
Ended ~2 years ago
Participants
104 active ยท 381 enrolled
Helping
Africa
Classification
Computer Vision
Health
Description

Tuberculosis (TB) is the ninth leading cause of death worldwide. In 2016, an estimated 417,000 people died from Tuberculosis in Africa, and 1.7 million globally. In South Africa Tuberculosis is the leading cause of death with 450,000 people developing the disease every year and 89,000 people dying from it. That’s ten people every hour.

With over 800,000 confirmed cases and 18,000 deaths recorded in Africa and over 17 million confirmed cases and 670,000 recorded deaths globally as of August 2, 2020, the COVID-19 global pandemic continues to take a heavy toll around the world. In countries with a high prevalence level of TB, TB can create additional complexity to the COVID-19 response. And by the same token, COVID-19 is adding new complexity to the ongoing battle against TB.

For both TB and COVID-19 patients, medical imaging (Chest X-Rays and/or CT Scans) can sometimes be performed to identify and manage any chest abnormalities that may develop.

This challenge asks you to build an AI model that can classify Tuberculosis and Normal X-Ray results. With Tuberculosis infections still active as the COVID-19 pandemic continues, an automated tool to help identify TB has the potential to reduce hospital workload and optimize patient care during a time when hospitals are being overwhelmed by COVID-19 cases.

Disclaimer

This challenge is solely for educational purposes. Chest X-Rays is one of various tools that can be used for triaging and screening for Tuberculosis. Furthermore, it is possible for an individual to develop different types of infections. This is to be treated purely as an exercise in tool development.

One potential follow up of this research is the detection of specific chest abnormalities associated with either COVID-19 or Tuberculosis, which could help in the development of personalised treatment for patients. Promising projects are eligible, but not guaranteed for additional mentoring into a publication.

Acknowledgements

Montgomery County X-ray Set: X-ray images in this data set have been acquired from the tuberculosis control program of the Department of Health and Human Services of Montgomery County, MD, USA.

Shenzhen Hospital X-ray Set: X-ray images in this data set have been collected by Shenzhen No.3 Hospital in Shenzhen, Guangdong province, China. The x-rays were acquired as part of the routine care at Shenzhen Hospital.

It is requested that publications resulting from the use of this data attribute the source (National Library of Medicine, National Institutes of Health, Bethesda, MD, USA and Shenzhen No.3 People’s Hospital, Guangdong Medical College, Shenzhen, China) and cite the following publications:

  • Jaeger S, Karargyris A, Candemir S, Folio L, Siegelman J, Callaghan F, Xue Z, Palaniappan K, Singh RK, Antani S, Thoma G, Wang YX, Lu PX, McDonald CJ. Automatic tuberculosis screening using chest radiographs. IEEE Trans Med Imaging. 2014 Feb;33(2):233-45. doi: 10.1109/TMI.2013.2284099. PMID: 24108713
  • Candemir S, Jaeger S, Palaniappan K, Musco JP, Singh RK, Xue Z, Karargyris A, Antani S, Thoma G, McDonald CJ. Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration. IEEE Trans Med Imaging. 2014 Feb;33(2):577-90. doi: 10.1109/TMI.2013.2290491. PMID: 24239990

About minoHealth (runchbase.com/organization/minohealth)

minoHealth is a startup and multifaceted system with the objective of democratising Quality Healthcare with innovative and cutting edge technologies like Artificial Intelligence, (Big) Data Analytics and Cloud computing in Africa. minoHealth‘s AI Research lab, minoHealth AI Labs researches and experiments with Artificial Intelligence and ways it can be applied to Healthcare to make it faster, better and yet cheaper. They research and apply Artificial Intelligence to fields like Biotechnology, Neuroscience, Optometry, Epidemiology and Dietetics/Nutrition. Their research projects include ‘ScaffoldNet: Detecting and Classifying Biomedical Polymer-Based Scaffolds via a Convolutional Neural Network’, published in the Scientific Book, “Advances In Information and Communication” and “End-to-End Learning via a Convolutional Neural Network for Cancer Cell Line Classification” published in Journal of Industry - University Collaboration. Their ongoing research projects also includes a collaboration with West African Centre for Cell Biology of Infectious Pathogens (WACCBIP) on the use of Machine Learning in the identification of a multivariate signature for Malaria Immunity. They are members of Massachusett Institute of Technology (MIT)'s Global ​BioSummit Community. They are also the lead for the Topic Group on Artificial Intelligence for Radiology, under the United Nations International Telecommunication Union (ITU) and World Health Organization (WHO) Focus Group on Artificial Intelligence for Health (FG-AI4H). minoHealth’s platform was listed in AppsAfrica’s ‘5 African innovations disrupting traditional sectors’.

About Runmila AI Institute (runmilainstitute.com)

reRunmila AI Institute is dedicated to preparing Ghana, and the rest of Africa for an Artificial Intelligence (AI) Future and the 4th Industrial Revolution. The world is drastically changing with all leading nations and blocs creating AI Plans & Strategies. Furthermore, plenty of these leading nations and blocs have already started implementing these strategies; Africa shouldn't be left behind. Through the skillful use of AI, we can leapfrog our development and solve many of Africa's grand challenges, especially connected to the 'Sustainable Development Goals (SDGs)'. Our goal is to train the talents that Africa requires to build this AI Future that the continent deserves and needs. We are doing this by equipping Africans, starting with Ghanaians, with practical skills in AI (Deep Learning & Machine Learning) and Data Science.

Our courses and workshops are practical, being Development-focused and designed around optimally teaching complex concepts in a fast and easy-to-grasp approach. Our students take away skills to develop their own AI projects that they can readily implement for their needs and demonstrate to would-be investors or employers. These courses and workshops are crafted by Artificial Intelligence Engineers and Researchers who are already working on real world applications of Artificial Intelligence in Africa.

Timeline

Competition closes on 15 November 2020.

Final submissions must be received by 11:59 PM GMT.

We reserve the right to update the contest timeline, if necessary.

Evaluation

The evaluation metric for this competition is the Area Under the ROC curve (AUC).

Your sample submission should look like, where LABEL is the probabiltiy that the lung x-ray is TB-positive. Please keep your values as probabilities.:

ID         LABEL
GTWSHFYQ   0.45   
QTFSSMGD   0.32
TBLBHSYT   0.91

Prizes

There are no cash prizes for this challenge.

However, the top 10 submissions will earn up to 2000 Zindi Points.

And the first 5 users/teams that beat benchmark_1 on the leaderboard will receive tickets to AI Expo Africa on 3-4 September 2020 (a completely virtual event this year), worth approx. $50 USD each.

Rules

This challenge is open to all and not restricted to any country.

Teams and collaboration

You may participate in this competition as an individual or in a team of up to four people. When creating a team, the team must have a total submission count less than or equal to the maximum allowable submissions as of the formation date. A team will be allowed the maximum number of submissions for the competition, minus the highest number of submissions among team members at team formation. Prizes are transferred only to the individual players or to the team leader.

Multiple accounts per user are not permitted, and neither is collaboration or membership across multiple teams. Individuals and their submissions originating from multiple accounts will be disqualified.

Code must not be shared privately outside of a team. Any code that is shared, must be made available to all competition participants through the platform. (i.e. on the discussion boards).

Datasets and packages

The solution must use publicly-available, open-source packages only. Your models should not use any of the metadata provided.

You may use only the datasets provided for this competition. Automated machine learning tools such as automl are not permitted.

If the challenge is a computer vision challenge, image metadata (Image size, aspect ratio, pixel count, etc) may not be used in your submission.

You may use pretrained models as long as they are openly available to everyone.

The data used in this competition is the sole property of Zindi and the competition host. You may not transmit, duplicate, publish, redistribute or otherwise provide or make available any competition data to any party not participating in the Competition (this includes uploading the data to any public site such as Kaggle or GitHub). You may upload, store and work with the data on any cloud platform such as Google Colab, AWS or similar, as long as 1) the data remains private and 2) doing so does not contravene Zindi’s rules of use.

You must notify Zindi immediately upon learning of any unauthorised transmission of or unauthorised access to the competition data, and work with Zindi to rectify any unauthorised transmission or access.

Your solution must not infringe the rights of any third party and you must be legally entitled to assign ownership of all rights of copyright in and to the winning solution code to Zindi.

Submissions and winning

You may make a maximum of 5 submissions per day. Your highest-scoring solution on the private leaderboard at the end of the competition will be the one by which you are judged.

As the challenge is now closed the number of submissions have been increased to 30 per day.

Zindi maintains a public leaderboard and a private leaderboard for each competition. The Public Leaderboard includes approximately 30% of the test dataset. While the competition is open, the Public Leaderboard will rank the submitted solutions by the accuracy score they achieve. Upon close of the competition, the Private Leaderboard, which covers the other 70% of the test dataset, will be made public and will constitute the final ranking for the competition.

If you are in the top 20 at the time the leaderboard closes, we will email you to request your code. On receipt of email, you will have 48 hours to respond and submit your code following the submission guidelines detailed below. Failure to respond will result in disqualification.

If your solution places 1st, 2nd, or 3rd on the final leaderboard, you will be required to submit your winning solution code to us for verification, and you thereby agree to assign all worldwide rights of copyright in and to such winning solution to Zindi.

If two solutions earn identical scores on the leaderboard, the tiebreaker will be the date and time in which the submission was made (the earlier solution will win).

You acknowledge and agree that Zindi may, without any obligation to do so, remove or disqualify an individual, team, or account if Zindi believes that such individual, team, or account is in violation of these rules. Entry into this competition constitutes your acceptance of these official competition rules.

Zindi is committed to providing solutions of value to our clients and partners. To this end, we reserve the right to disqualify your submission on the grounds of usability or value. This includes but is not limited to the use of data leaks or any other practices that we deem to compromise the inherent value of your solution.

Zindi also reserves the right to disqualify you and/or your submissions from any competition if we believe that you violated the rules or violated the spirit of the competition or the platform in any other way. The disqualifications are irrespective of your position on the leaderboard and completely at the discretion of Zindi.

Please refer to the FAQs and Terms of Use for additional rules that may apply to this competition. We reserve the right to update these rules at any time.

Reproducibility

  • If your submitted code does not reproduce your score on the leaderboard, we reserve the right to adjust your rank to the score generated by the code you submitted.
  • If your code does not run you will be dropped from the top 10. Please make sure your code runs before submitting your solution.
  • Always set the seed. Rerunning your model should always place you at the same position on the leaderboard. When running your solution, if randomness shifts you down the leaderboard we reserve the right to adjust your rank to the closest score that your submission reproduces.
  • We expect full documentation. This includes:
  • All data used
  • Output data and where they are stored
  • Explanation of features used
  • Your solution must include the original data provided by Zindi and validated external data (no processed data)
  • All editing of data must be done in a notebook (i.e. not manually in Excel)

Data standards:

  • Your submitted code must run on the original train, test, and other datasets provided.
  • Packages:
  • You must submit a requirements file that declares package versions. If a requirements file is not produced the solution will be run on the most up to date packages. Custom packages in your submission notebook will not be accepted.
  • You may only use tools available to everyone i.e. no paid services or free trials that require a credit card.

Consequences of breaking any rules of the competition or submission guidelines:

  • First offence: No prizes or points for 6 months. If you are caught cheating all individuals involved in cheating will be disqualified from the challenge(s) you were caught in and you will be disqualified from winning any competitions or Zindi points for the next six months.
  • Second offence: Banned from the platform. If you are caught for a second time your Zindi account will be disabled and you will be disqualified from winning any competitions or Zindi points using any other account.

Monitoring of submissions

  • We will review the top 20 solutions of every competition when the competition ends.
  • We reserve the right to request code from any user at any time during a challenge. You will have 24 hours to submit your code following the rules for code review (see above).
  • If you do not submit your code within 24 hours you will be disqualified from winning any competitions or Zindi points for the next six months. If you fall under suspicion again and your code is requested and you fail to submit your code within 24 hours, your Zindi account will be disabled and you will be disqualified from winning any competitions or Zindi points.

Zindi reserves the right to update these rules at any time.