VisioMel Challenge: Predicting Melanoma Relapse

Use digitized microscopic slides to predict the likelihood of melanoma relapse within the next five years. #health

€25,000 in prizes
may 2023
542 joined



Melanoma is a cancer of the skin which develops from cells responsible for skin pigmentation. In 2020, over 325,000 people were diagnosed with skin melanoma, with 57,000 deaths in the same year.1 Melanomas represent 10% of all skin cancers and are the most dangerous due to high likelihood of metastasizing (spreading).2

Patients are initially diagnosed with melanoma after a pathologist examines a portion of the cancerous tissue under a microscope. At this stage, the pathologist assesses the risk of relapse—a return of cancerous cells after the melanoma has been treated—based on information such as the thickness of the tumor and the presence of an ulceration. Combined with factors such as age, sex, and medical history of the patient, these microscopic observations can help a dermatologist assess the severity of the disease and determine appropriate surgical and medical treatment. Preventative treatments can be administered to patients with high likelihood for relapse. However, these are costly and expose patients to significant drug toxicity.

Assessing the risk of relapse therefore a vital but difficult task. It requires specialized training and careful examination of microscopic tissue. Currently, machine learning approaches can help analyze whole slide images (WSIs) for basic tasks like measuring area. However, computer vision has also shown some potential in classifying tumor subtypes, and in time may serve as a powerful tool to aid pathologists in making the most accurate diagnosis and prognosis. 3, 4, 5

Your goal in this challenge is to predict whether a relapse will occur in the 5 years following the initial diagnosis using digitized versions of microscopic slides.

Additional notes

Challenge data: Whole slide images are digital formats that allow glass slides to be viewed, managed, shared, and analyzed. These extremely high resolution images can be quite large. You can find more information and tips for working with the data on the data resources page.

External data & annotation: As noted in the challenge rules, external data and pre-trained models are allowed in this competition as long as they are freely and publicly available. In addition, participants are welcome to add their own private annotations to the challenge data. At the end of the challenge, top-performing participants will need to publicly share any private annotations and approaches in order to be eligible for a prize.

Research note: This challenge aims to engage pathologists, data scientists, and developers in working with a new dataset for research. As with any research dataset like this one, initial algorithms may pick up on correlations that are incidental to diagnosis. Solutions in this challenge are intended to serve as a starting point for continued research and development. The challenge organizers intend to make the collection of WSI data available online after the competition for ongoing improvement.

Competition End Date:

May 11, 2023, 11:59 p.m. UTC

Place Prize Amount
1st €12,000
2nd €8,000
3rd €5,000

Competition winners will also be invited to present their models in-person or remotely at an event organized by VisioMel in Paris on May 25, 2023.

How to compete

  1. Click the "Compete!" button in the sidebar to enroll in the competition.
  2. Get familiar with the problem through the overview and problem description. You might also want to reference additional resources available on the about page.
  3. Download the data from the data download tab.
  4. Create and train your own model. Check out the benchmark blog post for a good place to start!
  5. Bundle your trained model and prediction code for evaluation in our cloud runtime. See the code submission format page for more detail.
  6. Click “Submit” in the sidebar, and then “Make new submission”. You’re in!

Organized by VisioMel

This challenge is organized by VisioMel, in partnership with the Health Data Hub. The primary sponsor of the challenge is the French Ministerial Delegation for Digital Health (Délégation Ministérielle au Numérique en Santé) in the context of the "Digital Health Acceleration Strategy" of the French government and the Public Investment Bank (BPI). VisioMel also received funding from Pierre Fabre, Bristol Myers Squibb, and MSD.

Challenge organizer logos

Banner image courtesy of VisioMel