ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection
The goal of this recurring challenge is to help participants develop image analysis tools to enable the automated diagnosis of melanoma from dermoscopic images.
This challenge is broken into three separate tasks:
- Task 1: Lesion Segmentation
- Task 2: Lesion Attribute Detection
- Task 3: Disease Classification
Each competitor may participate in any or all of these tasks.
In each task, participants are asked to submit automated predictions on a held-out test set by July 27th, 11:59:59pm EDT. In addition to submitting predictions, each competitor is required to submit a link to a single 4-page manuscript describing the methods used to generate predictions. Participants may combine descriptions for multiple tasks into a single manuscript.
Cash prizes of $2500 USD will be awarded to winners of each of the tasks. The monetary prizes for the winners of the challenge will be awarded at the time of the MICCAI Workshop in Granada on September 20, 2018. The prizes are being provided by Canfield Scientific, Inc., a US company, and are subject to any restrictions incumbent on the sponsor. Winners will be asked to identify a recipient individual or entity who will be required to provide tax documentation (U.S. citizens- IRS form W-9, non-U.S. citizens Form W-8 BEN).
About the ISIC Archive
The International Skin Imaging Collaboration (ISIC) is an international effort to improve melanoma diagnosis, sponsored by the International Society for Digital Imaging of the Skin (ISDIS). The ISIC Archive contains the largest publicly available collection of quality controlled dermoscopic images of skin lesions.
Presently, the ISIC Archive contains over 13,000 dermoscopic images, which were collected from leading clinical centers internationally and acquired from a variety of devices within each center. Broad and international participation in image contribution is designed to insure a representative clinically relevant sample.
All incoming images to the ISIC Archive are screened for both privacy and quality assurance. Most images have associated clinical metadata, which has been vetted by recognized melanoma experts. A subset of the images have undergone annotation and markup by recognized skin cancer experts. These markups include dermoscopic features (i.e., global and focal morphologic elements in the image known to discriminate between types of skin lesions).
Skin cancer is a major public health problem, with over 5,000,000 newly diagnosed cases in the United States every year. Melanoma is the deadliest form of skin cancer, responsible for an overwhelming majority of skin cancer deaths. In 2015, the global incidence of melanoma was estimated to be over 350,000 cases, with almost 60,000 deaths. Although the mortality is significant, when detected early, melanoma survival exceeds 95%.
As pigmented lesions occurring on the surface of the skin, melanoma is amenable to early detection by expert visual inspection. It is also amenable to automated detection with image analysis. Given the widespread availability of high-resolution cameras, algorithms that can improve our ability to screen and detect troublesome lesions can be of great value. As a result, many centers have begun their own research efforts on automated analysis. However, a centralized, coordinated, and comparative effort across institutions has yet to be implemented.
Dermoscopy is an imaging technique that eliminates the surface reflection of skin. By removing surface reflection, visualization of deeper levels of skin is enhanced. Prior research has shown that when used by expert dermatologists, dermoscopy provides improved diagnostic accuracy, in comparison to standard photography. As inexpensive consumer dermatoscope attachments for smart phones are beginning to reach the market, the opportunity for automated dermoscopic assessment algorithms to positively influence patient care increases.
- Canfield Scientific
- Harald Kittler, M.D. Medical University of Vienna, Vienna, Austria
Computer Vision Chair
- Noel C. F. Codella, Ph.D. IBM Research, New York, USA
- M. Emre Celebi, Ph.D. University of Central Arkansas, Conway, Arkansas
- Kristin Dana, Ph.D. Rutgers University, New Jersey, USA
- David Gutman, Ph.D. Emory University, Atlanta, USA
- Allan Halpern, M.D. Memorial Sloan Kettering Cancer Center, New York, USA
- Brian Helba Kitware, Inc., New York, USA
- Philipp Tschandl, M.D. Ph.D. Medical University of Vienna, Vienna, Austria; Postdoctoral fellow at Simon Fraser University, British Columbia, Canada, supported by a one-year unrestricted research grant from MetaOptima Technology Inc.
Note: Any organizations/companies affiliated with members of the organizing committee are not excluded from participation in the Challenge, but must assure that their submissions are completely independent of the members of the organizing committee.
April 2nd, 2018:
- Training data release
July 9th, 2018:
- Validation and test data release
July 27th, 2018:
- Test results and associated manuscript submission deadline: 11:59:59pm EDT
August 3rd, 2018:
- Winners announced, and speaker invitations sent
September 20, 2018:
Time to final submission